CN114120122A - Remote sensing image-based disaster damage identification method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a remote sensing image-based disaster damage identification method, which comprises the following steps: inputting the training remote sensing image set after data enhancement into a disaster recognition model to obtain a disaster degree category, counting the number of pixel points in the training remote sensing image under the disaster degree category, calculating a first distribution value and a second distribution value corresponding to the disaster degree, calculating a preset loss weight value of the first distribution value and the second distribution value, performing weighted accumulation on the preset loss weight value and the loss value to obtain a final loss value, adjusting the disaster recognition model according to the final loss value to obtain a standard disaster recognition model, and inputting the remote sensing image to be recognized into the standard disaster recognition model to obtain a disaster recognition result. In addition, the invention also relates to a block chain technology, and the first distribution value can be stored in a node of the block chain. The invention also provides a remote sensing image-based disaster damage identification device, electronic equipment and a storage medium. The invention can improve the efficiency of disaster damage identification.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a remote sensing image-based disaster damage identification method and device, electronic equipment and a computer-readable storage medium.
Background
Various natural disasters occur more and more frequently in the world at present, and the natural disasters which occur every year can bring serious threats to national economy and the economic and property safety of people of various countries. Therefore, different areas where disasters occur each time and corresponding disaster damage degrees need to be analyzed, and follow-up better monitoring and prevention are facilitated. The existing disaster identification method usually judges the target loss situation according to the site survey of a salesman, is not intelligent enough, consumes manpower, and has low accuracy and efficiency for identifying the disaster. Therefore, a more efficient method for identifying a disaster is urgently needed.
Disclosure of Invention
The invention provides a remote sensing image-based disaster identification method and device and a computer-readable storage medium, and mainly aims to improve the efficiency of disaster identification.
In order to achieve the above object, the present invention provides a remote sensing image-based disaster identification method, including:
acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image;
counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, and calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points;
calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight value assigning method;
respectively calculating loss values corresponding to a preset number of loss functions based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value;
adjusting the disaster identification model according to the final loss value and a preset loss threshold value to obtain a standard disaster identification model;
and acquiring a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
Optionally, the inputting the training remote sensing image set into a preset disaster recognition model to obtain a disaster degree category corresponding to the training remote sensing image set, including:
performing convolution processing and pooling processing on the training remote sensing image set by using a compression channel in the disaster identification model to obtain an initial pooling image set;
performing deconvolution operation on the initial pooled image set to obtain a deconvolution image set;
performing image splicing on the initial pooling image set and the deconvolution image set, and performing feature extraction on the spliced image set to obtain a feature image set;
and inputting the characteristic image set to an output layer in the disaster damage recognition model to obtain the disaster damage degree category corresponding to the training remote sensing image set training remote sensing image.
Optionally, the counting the number of pixel points corresponding to the training remote sensing image set in the damage degree category includes:
determining the areas of the disaster degree categories corresponding to different training remote sensing images in the training remote sensing image set;
and identifying and summarizing the pixel points in the areas with different disaster degree categories to obtain the number of the pixel points corresponding to the different disaster degree categories.
Optionally, the calculating a first distribution value and a second distribution value corresponding to the damage degree based on the number of the pixel points includes:
utilizing a preset first distribution value formula and a preset second distribution value formula;
taking the number of the pixel points and the pre-acquired category number of the disaster damage degree as the input of the first step value formula to obtain a first distribution value;
and taking the category number of the damage degree as the input of the second distribution value formula to obtain a second distribution value.
Optionally, the preset first distribution value formula is:
wherein s1 is the first distribution value, wiAnd expressing the number of the ith type of the pixels with the damage degree, and expressing the number of the types of the damage degree by k.
Optionally, the calculating, by using a preset weight assignment method, a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value includes:
multiplying the second distribution value by a preset first reference value to obtain a first standard value, and if the first distribution value is greater than or equal to the first standard value and less than or equal to the second distribution value, determining the preset loss weight value as a first combined value; or
Multiplying the second distribution value by a preset second reference value to obtain a second standard value, and if the first distribution value is greater than or equal to the second standard value and less than or equal to the first standard value, determining the preset loss weight value as a second combined value; or
Multiplying the second distribution value by a preset third reference value to obtain a third standard value, and if the first distribution value is greater than or equal to the third standard value and less than or equal to the second standard value, determining the preset loss weight value as a third combined value; or
And if the first distribution value is greater than or equal to a preset fourth standard value and less than or equal to the third standard value, determining the preset loss weight value as a fourth combined value.
Optionally, the data enhancement of the original remote sensing image set to obtain a training remote sensing image set includes:
detecting missing remote sensing images in the original remote sensing image set, and executing deletion operation on the missing remote sensing images to obtain an initial remote sensing image set;
and carrying out image rotation, image translation and image scaling operation on the initial remote sensing image in the initial remote sensing image set to obtain a training remote sensing image set.
In order to solve the above problem, the present invention further provides a remote sensing image-based damage recognition apparatus, including:
the data enhancement module is used for acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
the class prediction module is used for inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree class corresponding to the training remote sensing image set training remote sensing image;
the model training module is used for counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points, calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method, calculating loss values corresponding to loss functions of preset numbers respectively based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value, and is used for adjusting the disaster recognition model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster recognition model;
and the disaster identification module is used for acquiring the remote sensing image to be identified, inputting the remote sensing image to be identified into the standard disaster identification model and obtaining a disaster identification result corresponding to the remote sensing image to be identified.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the remote sensing image-based disaster identification method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, where the at least one computer program is executed by a processor in an electronic device to implement the remote sensing image-based disaster identification method described above.
According to the embodiment of the invention, the data enhancement is carried out on the obtained original remote sensing image set, so that the richness of the obtained data of the training remote sensing image set can be improved, and the robustness of the subsequent model training is enhanced. Identifying a disaster degree category corresponding to the training remote sensing image set by using a disaster recognition model, calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, wherein the first distribution value and the second distribution value reflect the distribution condition of the disaster degree, the accuracy of the loss function and the accuracy of the model training can be improved by calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value and constructing the loss function according to the preset loss weight values, and adjusting the disaster identification model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster identification model, identifying the remote sensing image to be identified and obtaining a disaster identification result corresponding to the remote sensing image to be identified. Therefore, the remote sensing image-based disaster identification method, the remote sensing image-based disaster identification device, the electronic equipment and the computer-readable storage medium can solve the problem that the efficiency of disaster identification is not high enough.
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Fig. 1 is a schematic flow chart of a remote sensing image-based disaster identification method according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a remote sensing image-based damage identification apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the remote sensing image-based disaster damage identification method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a remote sensing image-based disaster damage identification method. The execution subject of the remote sensing image-based disaster damage identification method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the remote sensing image-based disaster identification method may be executed by software or hardware installed in a terminal device or a server device, where the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart of a remote sensing image-based disaster identification method according to an embodiment of the present invention. In this embodiment, the method for identifying a disaster based on a remote sensing image includes:
s1, obtaining an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set.
In the embodiment of the invention, the original Remote Sensing Image set comprises a large number of original Remote Sensing images, wherein the original Remote Sensing images (Remote Sensing images) refer to films or photos recording electromagnetic waves of various ground features and are mainly divided into aerial photos and satellite photos. The remote sensing image with the resolution of 16 meters of the high-resolution one-number multispectral camera WFV can be acquired from a Chinese resource satellite application center.
Specifically, the data enhancement of the original remote sensing image set to obtain a training remote sensing image set includes:
detecting missing remote sensing images in the original remote sensing image set, and executing deletion operation on the missing remote sensing images to obtain an initial remote sensing image set;
and carrying out image rotation, image translation and image scaling operation on the initial remote sensing image in the initial remote sensing image set to obtain a training remote sensing image set.
In detail, the missing remote sensing images in the original remote sensing image set may be images with serious image information missing.
And performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set, enriching the number of images in the training remote sensing image set and improving the robustness of subsequent model training.
And S2, inputting the training remote sensing image set into a preset disaster recognition model to obtain the disaster degree category corresponding to the training remote sensing image set.
In the embodiment of the invention, the disaster identification model can be a network such as res-net, U-net, depeplab-v 3 and the like. In the scheme, the disaster damage identification model is a U-net network. The U-net network is formed by a compression channel on the left half and an expansion channel on the right half together into a shape of a letter U. Wherein the compression channel is a typical convolutional neural network structure.
Specifically, the inputting the training remote sensing image set into a preset disaster recognition model to obtain a disaster degree category corresponding to the training remote sensing image set, includes:
performing convolution processing and pooling processing on the training remote sensing image set by using a compression channel in the disaster identification model to obtain an initial pooling image set;
performing deconvolution operation on the initial pooled image set to obtain a deconvolution image set;
performing image splicing on the initial pooling image set and the deconvolution image set, and performing feature extraction on the spliced image set to obtain a feature image set;
and inputting the characteristic image set to an output layer in the disaster damage recognition model to obtain the disaster damage degree category corresponding to the training remote sensing image set training remote sensing image.
In detail, the damage identification model is composed of a left half compression channel (compressing Path) and a right half expansion channel (expanding Path). The compressed channel is a typical convolutional neural network structure, which repeatedly adopts a structure of 2 convolutional layers and 1 maximal pooling layer, and the dimension of the feature map increases by 1 time after each pooling operation. In the expansion channel, 1 time of deconvolution operation is firstly carried out to reduce the dimension of the feature graph by half, then the feature graph obtained by cutting the corresponding compression channel is spliced to form a feature graph with the size 2 times of that of the feature graph again, then 2 convolution layers are adopted to carry out feature extraction, and the structure is repeated. At the final output level, the 64-dimensional feature map is mapped into a 2-dimensional output map using 2 convolutional layers. The output graph comprises the disaster degree category.
The damage degree category refers to the severity of the damage caused by the disaster, and can be divided into a first-level damage, a second-level damage, a third-level damage and the like.
S3, counting the number of pixel points corresponding to the training remote sensing image set under the damage degree category, and calculating a first distribution value and a second distribution value corresponding to the damage degree based on the number of the pixel points.
In the embodiment of the present invention, the counting the number of pixel points corresponding to the training remote sensing image set in the damage degree category includes:
determining the areas of the disaster degree categories corresponding to different training remote sensing images in the training remote sensing image set;
and identifying and summarizing the pixel points in the areas with different disaster degree categories to obtain the number of the pixel points corresponding to the different disaster degree categories.
In detail, the disaster degree category corresponding to the training remote sensing image comprises a first-level disaster damage, a second-level disaster damage or a third-level disaster damage marked on the remote sensing image, the areas of the first-level disaster damage, the second-level disaster damage and the third-level disaster damage are respectively determined, and the number of pixel points of the areas of different disaster damages is identified and counted.
Specifically, the calculating a first distribution value and a second distribution value corresponding to the damage degree based on the number of the pixel points includes:
utilizing a preset first distribution value formula and a preset second distribution value formula;
taking the number of the pixel points and the pre-acquired category number of the disaster damage degree as the input of the first step value formula to obtain a first distribution value;
and taking the category number of the damage degree as the input of the second distribution value formula to obtain a second distribution value.
Further, the preset first distribution value formula is as follows:
wherein s1 is the first distribution value, wiAnd expressing the number of the ith type of the pixels with the damage degree, and expressing the number of the types of the damage degree by k.
Specifically, the preset second distribution value formula is:
where s2 is the second distribution value, and k represents the number of categories of the damage degree.
And S4, calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method.
In an embodiment of the present invention, the calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method includes:
multiplying the second distribution value by a preset first reference value to obtain a first standard value, and if the first distribution value is greater than or equal to the first standard value and less than or equal to the second distribution value, determining the preset loss weight value as a first combined value; or
Multiplying the second distribution value by a preset second reference value to obtain a second standard value, and if the first distribution value is greater than or equal to the second standard value and less than or equal to the first standard value, determining the preset loss weight value as a second combined value; or
Multiplying the second distribution value by a preset third reference value to obtain a third standard value, and if the first distribution value is greater than or equal to the third standard value and less than or equal to the second standard value, determining the preset loss weight value as a third combined value; or
And if the first distribution value is greater than or equal to a preset fourth standard value and less than or equal to the third standard value, determining the preset loss weight value as a fourth combined value.
Preferably, the first reference value is 0.75, the second reference value is 0.5, the third reference value is 0.25, the fourth reference value is 0, the first combined value is (0.15, 0.35, 0.15, 0.35), the second combined value is (0.25, 0.25, 0.25, 0.25), the third combined value is (0.35, 0.15, 0.35, 0.15), the fourth combined value is (0.5, 0, 0.5, 0).
And S5, calculating loss values corresponding to a preset number of loss functions respectively based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight value and the loss value corresponding to the loss function to obtain a final loss value.
In the embodiment of the present invention, the preset number of Loss functions may be four different Loss functions, which are respectively a CE Loss function, a Focal Loss function, a Dice Loss function, and a Lovasz Loss function.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the CE loss function in the preset number of loss functions is:
wherein M represents the number of categories of the degree of damage, pcIs the damage degree category, ycAnd the real damage category is obtained.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Focal loss function in the loss functions with the preset number is as follows:
wherein, p is the disaster degree category, and alpha and gamma are fixed parameters.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Dice Loss function in the Loss functions with the preset number is as follows:
and TP, FP and FN respectively represent the number of pixels with true positive, false positive and false negative in the damage degree category.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Lovasz loss function in the loss functions with the preset number is as follows:
wherein, y*In order to be in the category of the damage degree,
and c is a fixed parameter for the real disaster damage category.
Specifically, the performing weighted accumulation on the preset loss weight value and the loss value corresponding to the loss function to obtain a final loss value includes:
and calculating to obtain a final loss value based on a preset final loss formula.
Further, the final loss formula is:
wherein L isFFor the final loss value, L, FL, s and
loss values, delta, epsilon, zeta and corresponding to a predetermined number of loss functions
Respectively, a plurality of preset loss weight values.
And S6, adjusting the disaster identification model according to the final loss value and the preset loss threshold value to obtain a standard disaster identification model.
In the embodiment of the present invention, the disaster recognition model is adjusted according to the final loss value and the preset loss threshold, when the final loss value is smaller than the loss threshold, the disaster recognition model is output as a standard disaster recognition model, when the final loss value is greater than or equal to the loss threshold, the model parameters of the disaster recognition model are adjusted until the final loss value is smaller than the loss threshold, and the disaster recognition model after the model parameters are adjusted is output as the standard disaster recognition model.
Wherein the model parameter may be a model weight or a model gradient.
And S7, obtaining a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
In the embodiment of the invention, the remote sensing image to be recognized is an image which needs to be subjected to disaster and damage recognition after a detected disaster occurs, and the remote sensing image to be recognized is input into the standard disaster and damage recognition model, so that a disaster and damage recognition result corresponding to the remote sensing image to be recognized can be obtained.
In detail, the disaster recognition result obtained by the recognition can identify the place where the disaster occurred, and focus on the area.
According to the embodiment of the invention, the data enhancement is carried out on the obtained original remote sensing image set, so that the richness of the obtained data of the training remote sensing image set can be improved, and the robustness of the subsequent model training is enhanced. Identifying a disaster degree category corresponding to the training remote sensing image set by using a disaster recognition model, calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, wherein the first distribution value and the second distribution value reflect the distribution condition of the disaster degree, the accuracy of the loss function and the accuracy of the model training can be improved by calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value and constructing the loss function according to the preset loss weight values, and adjusting the disaster identification model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster identification model, identifying the remote sensing image to be identified and obtaining a disaster identification result corresponding to the remote sensing image to be identified. Therefore, the remote sensing image-based disaster identification method provided by the invention can solve the problem that the efficiency of disaster identification is not high enough.
Fig. 2 is a functional block diagram of a remote sensing image-based damage identification apparatus according to an embodiment of the present invention.
The remote sensing image-based damage recognition device 100 of the present invention may be installed in an electronic device. According to the realized functions, the remote sensing image-based damage recognition device 100 may include a data enhancement module 101, a category prediction module 102, a model training module 103, and a damage recognition module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data enhancement module 101 is configured to obtain an original remote sensing image set, and perform data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
the category prediction module 102 is configured to input the training remote sensing image set into a preset disaster identification model, so as to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image;
the model training module 103 is configured to count the number of pixels corresponding to the training remote sensing image set in the damage degree category, calculate a first distribution value and a second distribution value corresponding to the damage degree based on the number of the pixels, calculate a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method, calculate loss values corresponding to a preset number of loss functions based on the damage degree category and a preset true damage category, respectively, perform weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value, and adjust the damage recognition model according to a size between the final loss value and a preset loss threshold value to obtain a standard damage recognition model;
and the damage identification module 104 is configured to acquire a remote sensing image to be identified, input the remote sensing image to be identified into the standard damage identification model, and obtain a damage identification result corresponding to the remote sensing image to be identified.
Specifically, the remote sensing image based damage recognition apparatus 100 has the following specific embodiments of the modules:
the method comprises the steps of firstly, obtaining an original remote sensing image set, and carrying out data enhancement on the original remote sensing image set to obtain a training remote sensing image set.
In the embodiment of the invention, the original Remote Sensing Image set comprises a large number of original Remote Sensing images, wherein the original Remote Sensing images (Remote Sensing images) refer to films or photos recording electromagnetic waves of various ground features and are mainly divided into aerial photos and satellite photos. The remote sensing image with the resolution of 16 meters of the high-resolution one-number multispectral camera WFV can be acquired from a Chinese resource satellite application center.
Specifically, the data enhancement of the original remote sensing image set to obtain a training remote sensing image set includes:
detecting missing remote sensing images in the original remote sensing image set, and executing deletion operation on the missing remote sensing images to obtain an initial remote sensing image set;
and carrying out image rotation, image translation and image scaling operation on the initial remote sensing image in the initial remote sensing image set to obtain a training remote sensing image set.
In detail, the missing remote sensing images in the original remote sensing image set may be images with serious image information missing.
And performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set, enriching the number of images in the training remote sensing image set and improving the robustness of subsequent model training.
And step two, inputting the training remote sensing image set into a preset disaster recognition model to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image.
In the embodiment of the invention, the disaster identification model can be a network such as res-net, U-net, depeplab-v 3 and the like. In the scheme, the disaster damage identification model is a U-net network. The U-net network is formed by a compression channel on the left half and an expansion channel on the right half together into a shape of a letter U. Wherein the compression channel is a typical convolutional neural network structure.
Specifically, the inputting the training remote sensing image set into a preset disaster recognition model to obtain a disaster degree category corresponding to the training remote sensing image set, includes:
performing convolution processing and pooling processing on the training remote sensing image set by using a compression channel in the disaster identification model to obtain an initial pooling image set;
performing deconvolution operation on the initial pooled image set to obtain a deconvolution image set;
performing image splicing on the initial pooling image set and the deconvolution image set, and performing feature extraction on the spliced image set to obtain a feature image set;
and inputting the characteristic image set to an output layer in the disaster damage recognition model to obtain the disaster damage degree category corresponding to the training remote sensing image set training remote sensing image.
In detail, the damage identification model is composed of a left half compression channel (compressing Path) and a right half expansion channel (expanding Path). The compressed channel is a typical convolutional neural network structure, which repeatedly adopts a structure of 2 convolutional layers and 1 maximal pooling layer, and the dimension of the feature map increases by 1 time after each pooling operation. In the expansion channel, 1 time of deconvolution operation is firstly carried out to reduce the dimension of the feature graph by half, then the feature graph obtained by cutting the corresponding compression channel is spliced to form a feature graph with the size 2 times of that of the feature graph again, then 2 convolution layers are adopted to carry out feature extraction, and the structure is repeated. At the final output level, the 64-dimensional feature map is mapped into a 2-dimensional output map using 2 convolutional layers. The output graph comprises the disaster degree category.
The damage degree category refers to the severity of the damage caused by the disaster, and can be divided into a first-level damage, a second-level damage, a third-level damage and the like.
And thirdly, counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, and calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points.
In the embodiment of the present invention, the counting the number of pixel points corresponding to the training remote sensing image set in the damage degree category includes:
determining the areas of the disaster degree categories corresponding to different training remote sensing images in the training remote sensing image set;
and identifying and summarizing the pixel points in the areas with different disaster degree categories to obtain the number of the pixel points corresponding to the different disaster degree categories.
In detail, the disaster degree category corresponding to the training remote sensing image comprises a first-level disaster damage, a second-level disaster damage or a third-level disaster damage marked on the remote sensing image, the areas of the first-level disaster damage, the second-level disaster damage and the third-level disaster damage are respectively determined, and the number of pixel points of the areas of different disaster damages is identified and counted.
Specifically, the calculating a first distribution value and a second distribution value corresponding to the damage degree based on the number of the pixel points includes:
utilizing a preset first distribution value formula and a preset second distribution value formula;
taking the number of the pixel points and the pre-acquired category number of the disaster damage degree as the input of the first step value formula to obtain a first distribution value;
and taking the category number of the damage degree as the input of the second distribution value formula to obtain a second distribution value.
Further, the preset first distribution value formula is as follows:
wherein s1 is the first distribution value, wiAnd expressing the number of the ith type of the pixels with the damage degree, and expressing the number of the types of the damage degree by k.
Specifically, the preset second distribution value formula is:
where s2 is the second distribution value, and k represents the number of categories of the damage degree.
And fourthly, calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight value assigning method.
In an embodiment of the present invention, the calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method includes:
multiplying the second distribution value by a preset first reference value to obtain a first standard value, and if the first distribution value is greater than or equal to the first standard value and less than or equal to the second distribution value, determining the preset loss weight value as a first combined value; or
Multiplying the second distribution value by a preset second reference value to obtain a second standard value, and if the first distribution value is greater than or equal to the second standard value and less than or equal to the first standard value, determining the preset loss weight value as a second combined value; or
Multiplying the second distribution value by a preset third reference value to obtain a third standard value, and if the first distribution value is greater than or equal to the third standard value and less than or equal to the second standard value, determining the preset loss weight value as a third combined value; or
And if the first distribution value is greater than or equal to a preset fourth standard value and less than or equal to the third standard value, determining the preset loss weight value as a fourth combined value.
Preferably, the first reference value is 0.75, the second reference value is 0.5, the third reference value is 0.25, the fourth reference value is 0, the first combined value is (0.15, 0.35, 0.15, 0.35), the second combined value is (0.25, 0.25, 0.25, 0.25), the third combined value is (0.35, 0.15, 0.35, 0.15), the fourth combined value is (0.5, 0, 0.5, 0).
And fifthly, calculating loss values corresponding to a preset number of loss functions respectively based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value.
In the embodiment of the present invention, the preset number of Loss functions may be four different Loss functions, which are respectively a CE Loss function, a Focal Loss function, a Dice Loss function, and a Lovasz Loss function.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the CE loss function in the preset number of loss functions is:
wherein M represents the number of categories of the degree of damage, pcIs the damage degree category, ycAnd the real damage category is obtained.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Focal loss function in the loss functions with the preset number is as follows:
wherein, p is the disaster degree category, and alpha and gamma are fixed parameters.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Dice Loss function in the Loss functions with the preset number is as follows:
and TP, FP and FN respectively represent the number of pixels with true positive, false positive and false negative in the damage degree category.
In an embodiment of the present invention, the calculating the loss values corresponding to the loss functions in the preset number based on the disaster degree category and the preset real disaster category includes:
the Lovasz loss function in the loss functions with the preset number is as follows:
wherein, y*In order to be in the category of the damage degree,
and c is a fixed parameter for the real disaster damage category.
Specifically, the performing weighted accumulation on the preset loss weight value and the loss value corresponding to the loss function to obtain a final loss value includes:
and calculating to obtain a final loss value based on a preset final loss formula.
Further, the final loss formula is:
wherein L isFFor the final loss value, L, FL, s and
loss values, delta, epsilon, zeta and corresponding to a predetermined number of loss functions
Respectively, a plurality of preset loss weight values.
And step six, adjusting the disaster recognition model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster recognition model.
In the embodiment of the present invention, the disaster recognition model is adjusted according to the final loss value and the preset loss threshold, when the final loss value is smaller than the loss threshold, the disaster recognition model is output as a standard disaster recognition model, when the final loss value is greater than or equal to the loss threshold, the model parameters of the disaster recognition model are adjusted until the final loss value is smaller than the loss threshold, and the disaster recognition model after the model parameters are adjusted is output as the standard disaster recognition model.
Wherein the model parameter may be a model weight or a model gradient.
And seventhly, acquiring a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
In the embodiment of the invention, the remote sensing image to be recognized is an image which needs to be subjected to disaster and damage recognition after a detected disaster occurs, and the remote sensing image to be recognized is input into the standard disaster and damage recognition model, so that a disaster and damage recognition result corresponding to the remote sensing image to be recognized can be obtained.
In detail, the disaster recognition result obtained by the recognition can identify the place where the disaster occurred, and focus on the area.
According to the embodiment of the invention, the data enhancement is carried out on the obtained original remote sensing image set, so that the richness of the obtained data of the training remote sensing image set can be improved, and the robustness of the subsequent model training is enhanced. Identifying a disaster degree category corresponding to the training remote sensing image set by using a disaster recognition model, calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, wherein the first distribution value and the second distribution value reflect the distribution condition of the disaster degree, the accuracy of the loss function and the accuracy of the model training can be improved by calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value and constructing the loss function according to the preset loss weight values, and adjusting the disaster identification model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster identification model, identifying the remote sensing image to be identified and obtaining a disaster identification result corresponding to the remote sensing image to be identified. Therefore, the remote sensing image-based disaster identification device provided by the invention can solve the problem that the efficiency of disaster identification is not high enough.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a remote sensing image-based disaster identification method according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program, such as a remote sensing image-based damage identification program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by operating or executing a program or a module stored in the memory 11 (for example, executing a remote sensing image-based disaster recognition program or the like) and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to temporarily store data that has been output or is to be output, as well as application software installed in the electronic device and various types of data, such as codes of a remote sensing image-based damage recognition program.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The remote sensing image-based damage identification program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when running in the processor 10, can implement:
acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image;
counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, and calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points;
calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight value assigning method;
respectively calculating loss values corresponding to a preset number of loss functions based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value;
adjusting the disaster identification model according to the final loss value and a preset loss threshold value to obtain a standard disaster identification model;
and acquiring a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image;
counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, and calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points;
calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight value assigning method;
respectively calculating loss values corresponding to a preset number of loss functions based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value;
adjusting the disaster identification model according to the final loss value and a preset loss threshold value to obtain a standard disaster identification model;
and acquiring a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A remote sensing image-based disaster damage identification method is characterized by comprising the following steps:
acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree category corresponding to the training remote sensing image set training remote sensing image;
counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, and calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points;
calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight value assigning method;
respectively calculating loss values corresponding to a preset number of loss functions based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value;
adjusting the disaster identification model according to the final loss value and a preset loss threshold value to obtain a standard disaster identification model;
and acquiring a remote sensing image to be recognized, inputting the remote sensing image to be recognized into the standard disaster recognition model, and obtaining a disaster recognition result corresponding to the remote sensing image to be recognized.
2. The remote sensing image-based damage recognition method according to claim 1, wherein the step of inputting the training remote sensing image set into a preset damage recognition model to obtain a damage degree category corresponding to the training remote sensing image set comprises the steps of:
performing convolution processing and pooling processing on the training remote sensing image set by using a compression channel in the disaster identification model to obtain an initial pooling image set;
performing deconvolution operation on the initial pooled image set to obtain a deconvolution image set;
performing image splicing on the initial pooling image set and the deconvolution image set, and performing feature extraction on the spliced image set to obtain a feature image set;
and inputting the characteristic image set to an output layer in the disaster damage recognition model to obtain the disaster damage degree category corresponding to the training remote sensing image set training remote sensing image.
3. The remote sensing image-based damage identification method according to claim 1, wherein the counting the number of pixels corresponding to the training remote sensing image set in the damage degree category comprises:
determining the areas of the disaster degree categories corresponding to different training remote sensing images in the training remote sensing image set;
and identifying and summarizing the pixel points in the areas with different disaster degree categories to obtain the number of the pixel points corresponding to the different disaster degree categories.
4. The remote-sensing-image-based damage identification method according to claim 1, wherein the calculating of the first distribution value and the second distribution value corresponding to the damage degree based on the number of the pixel points comprises:
utilizing a preset first distribution value formula and a preset second distribution value formula;
taking the number of the pixel points and the pre-acquired category number of the disaster damage degree as the input of the first step value formula to obtain a first distribution value;
and taking the category number of the damage degree as the input of the second distribution value formula to obtain a second distribution value.
5. The remote-sensing-image-based damage identification method according to claim 4, wherein the preset first distribution value formula is as follows:
wherein s1 is the first distribution value, wiAnd expressing the number of the ith type of the pixels with the damage degree, and expressing the number of the types of the damage degree by k.
6. The remote-sensing-image-based damage identification method according to claim 1, wherein the calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method includes:
multiplying the second distribution value by a preset first reference value to obtain a first standard value, and if the first distribution value is greater than or equal to the first standard value and less than or equal to the second distribution value, determining the preset loss weight value as a first combined value; or
Multiplying the second distribution value by a preset second reference value to obtain a second standard value, and if the first distribution value is greater than or equal to the second standard value and less than or equal to the first standard value, determining the preset loss weight value as a second combined value; or
Multiplying the second distribution value by a preset third reference value to obtain a third standard value, and if the first distribution value is greater than or equal to the third standard value and less than or equal to the second standard value, determining the preset loss weight value as a third combined value; or
And if the first distribution value is greater than or equal to a preset fourth standard value and less than or equal to the third standard value, determining the preset loss weight value as a fourth combined value.
7. The remote-sensing-image-based damage identification method according to any one of claims 1 to 6, wherein the data enhancement of the original remote-sensing image set to obtain a training remote-sensing image set comprises:
detecting missing remote sensing images in the original remote sensing image set, and executing deletion operation on the missing remote sensing images to obtain an initial remote sensing image set;
and carrying out image rotation, image translation and image scaling operation on the initial remote sensing image in the initial remote sensing image set to obtain a training remote sensing image set.
8. A remote sensing image-based disaster damage identification device is characterized in that the device comprises:
the data enhancement module is used for acquiring an original remote sensing image set, and performing data enhancement on the original remote sensing image set to obtain a training remote sensing image set;
the class prediction module is used for inputting the training remote sensing image set into a preset disaster identification model to obtain a disaster degree class corresponding to the training remote sensing image set training remote sensing image;
the model training module is used for counting the number of pixel points corresponding to the training remote sensing image set under the disaster degree category, calculating a first distribution value and a second distribution value corresponding to the disaster degree based on the number of the pixel points, calculating a plurality of preset loss weight values corresponding to the first distribution value and the second distribution value by using a preset weight assignment method, calculating loss values corresponding to loss functions of preset numbers respectively based on the disaster degree category and a preset real disaster category, and performing weighted accumulation on the preset loss weight values and the loss values corresponding to the loss functions to obtain a final loss value, and is used for adjusting the disaster recognition model according to the size between the final loss value and a preset loss threshold value to obtain a standard disaster recognition model;
and the disaster identification module is used for acquiring the remote sensing image to be identified, inputting the remote sensing image to be identified into the standard disaster identification model and obtaining a disaster identification result corresponding to the remote sensing image to be identified.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method for remote sensing image based disaster identification according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for remote sensing image-based damage identification according to any one of claims 1 to 7.
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