CN112966548A - Soybean plot identification method and system - Google Patents
Soybean plot identification method and system Download PDFInfo
- Publication number
- CN112966548A CN112966548A CN202110060578.6A CN202110060578A CN112966548A CN 112966548 A CN112966548 A CN 112966548A CN 202110060578 A CN202110060578 A CN 202110060578A CN 112966548 A CN112966548 A CN 112966548A
- Authority
- CN
- China
- Prior art keywords
- soybean
- plot
- identification
- data
- land
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 244000068988 Glycine max Species 0.000 title claims abstract description 180
- 235000010469 Glycine max Nutrition 0.000 title claims abstract description 180
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000005070 sampling Methods 0.000 claims abstract description 46
- 238000012549 training Methods 0.000 claims abstract description 36
- 230000011218 segmentation Effects 0.000 claims description 51
- 238000004458 analytical method Methods 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000013179 statistical model Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 4
- 238000013136 deep learning model Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 description 8
- 238000000638 solvent extraction Methods 0.000 description 6
- 240000008042 Zea mays Species 0.000 description 5
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 5
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 5
- 238000004891 communication Methods 0.000 description 5
- 235000005822 corn Nutrition 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000011835 investigation Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 238000012732 spatial analysis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Multimedia (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a soybean plot identification method and a system, wherein the method comprises the following steps: acquiring a remote sensing image picture of a region to be identified; inputting the remote sensing image picture into an identification model, and outputting a soybean plot identification result of the region; the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one. The method realizes high identification precision and high accuracy of space positioning for obtaining the soybean planting land blocks based on the satellite remote sensing data, and meets the requirements of practical application.
Description
Technical Field
The invention relates to the technical field of agricultural image identification, in particular to a soybean plot identification method and system.
Background
Innovative regional income insurance developed in recent years in China is introduced into satellite remote sensing technology to assist the work of underwriting, investigation, loss assessment and loss assessment of agricultural insurance, wherein the verification of the guaranteed area of crops is an important part in the implementation process of agricultural insurance, and the extraction of crops is mostly concentrated on pixel-level identification in the current application and is not combined with land parcel data, so that the estimation precision of the crop planting area is not high, the accuracy of space positioning is poor, the inconvenience is brought to the on-site investigation work, and the application requirements of underwriting and loss assessment work of insurance companies cannot be met.
Disclosure of Invention
The invention provides a soybean plot identification method and a soybean plot identification system, which are used for solving the problems that the identification precision and the space positioning of a soybean planting plot obtained based on satellite remote sensing data in the prior art cannot meet the application requirements, and realize higher identification precision and space positioning accuracy.
The invention provides a soybean plot identification method, which comprises the following steps:
acquiring a remote sensing image picture of a region to be identified;
inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified;
the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
According to the soybean plot identification method provided by the invention, the identification model comprises a plot segmentation model, a soybean identification model and a spatial statistic model;
inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified, wherein the identification result comprises the following steps:
inputting the remote sensing image picture into the land block segmentation model, and outputting a segmented land block image and a segmented land block vector layer in the whole region;
inputting the segmented plot images into the soybean identification model, and outputting the crop category data map layers of all pixel points on the plots in the whole region;
inputting the segmented plot vector layer in the whole region and the crop category data layer of each pixel point on the plot in the whole region into the space statistical model for superposition analysis, and judging the soybean plot and counting the soybean planting area according to the result of the superposition analysis.
According to the soybean plot identification method provided by the invention, the segmented plot vector map layer in the whole region and the crop category data map layer of each pixel point on the plot in the whole region are input into the space statistical model for superposition analysis, and the method comprises the following steps:
and superposing layer data of corresponding coordinate points one by one on the segmented block vector layer in the whole region and the crop category data layer of each pixel point on the block in the whole region according to the geographic coordinate information.
According to the soybean plot identification method provided by the invention, a sample image of a soybean sampling plot comprises a remote sensing image picture, radar image data and soybean sampling plot vector data;
the identification model is obtained after training based on a sample image of a soybean sampling land and a corresponding identification label, and comprises the following steps:
selecting a remote sensing image picture of a partial area for label processing and then taking the remote sensing image picture as a training sample image to train the land parcel segmentation model;
inputting the remote sensing image picture of the whole area as a verification sample image into the trained plot segmentation model to obtain the data of the segmented plot;
training the soybean recognition model by using the radar image data, the soybean sampling plot vector data and the segmented plot data;
wherein the soybean sampled block vector data is obtained by field sampling.
According to the soybean plot identification method provided by the invention, the remote sensing image picture of the selected partial area is subjected to label processing and then used as a training sample image to train the plot segmentation model, and the method comprises the following steps:
selecting a target remote sensing image picture from the remote sensing image pictures of the whole area to edit and manufacture an identification label of a plot sample and a plot segmentation remote sensing image, and creating an identification label data set according to the identification label of the plot sample; the land block segmentation remote sensing image corresponds to the identification labels of the land block samples one by one;
and inputting the identification label of the land sample and the land segmentation remote sensing image corresponding to the identification label into a land segmentation model so as to realize semantic segmentation training of the land segmentation model.
According to the soybean parcel identification method provided by the invention, the training of the soybean identification model by using radar image data, soybean sampling parcel vector data and the partitioned parcel data comprises the following steps:
rasterizing the vector data of the soybean sampling land blocks and the segmented land block data according to the resolution of the radar image data to obtain a raster data set;
extracting time sequence radar data corresponding to each pixel point from each raster data in the raster data set to obtain a radar data set of the soybean identification sample and a radar data set of each pixel point on the land parcel; the time sequence radar data corresponding to each pixel point comprises geographic coordinate information of the radar for spatial superposition analysis;
and training a time sequence deep learning model by using the soybean identification sample radar data set to obtain the soybean identification model, and identifying and predicting the radar data set of each pixel point on the land parcel.
According to the soybean plot identification method provided by the invention, the segmented plot vector layer in the whole region is obtained by mapping the segmented plot data and the remote sensing image picture of the whole region according to pixels and adding geographic coordinate information based on the plot segmentation model;
and the crop category data map layer of each pixel point on the plot in the whole region is obtained by identifying and predicting the radar data set of each pixel point on the plot based on the soybean identification model.
The invention also provides a soybean plot identification system, which comprises a data input module and an identification module;
the data input module is used for acquiring a remote sensing image picture of a region to be identified;
the identification module is used for inputting the remote sensing image picture and outputting an identification result of the soybean plot of the area to be identified;
the identification module is obtained by training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the soybean plot identification method.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for identifying soybean blocks as described in any one of the above.
According to the soybean plot identification method and system provided by the invention, the problem that the identification of the soybean planting plot obtained based on satellite remote sensing data cannot meet the application requirements in terms of precision and space positioning can be effectively solved through a plot segmentation method based on a high-resolution remote sensing image and a soybean identification method based on time sequence radar data.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method for identifying soybean plots provided by the present invention;
FIG. 2 is a second schematic flow chart of the method for identifying soybean plots provided by the present invention;
FIG. 3 is a third schematic flow chart of the soybean plot identification method provided by the present invention;
FIG. 4 is a schematic diagram of a plot partitioning tag dataset provided by the present invention;
FIG. 5 is a schematic view of a land segmentation remote sensing image dataset provided by the present invention;
FIG. 6 is a sample view of a portion of a remote sensing image picture provided by the present invention;
FIG. 7 is a partial sample view of a remote sensing image picture matching geographic coordinate information as provided by the present invention;
FIG. 8 is a schematic view of the structure of a soybean plot identification system provided by the present invention;
FIG. 9 is a schematic view of a crop category planting area provided by the present invention;
FIG. 10 is a schematic structural diagram of an electronic device provided by the present invention;
reference numerals:
1: a soybean planting area; 2: a soybean planting area; 3: a corn planting area;
4: a corn planting area; 5: and (4) a corn planting area.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following describes a method for identifying a soybean parcel provided by the present invention with reference to fig. 1 to 7.
The invention provides a soybean plot identification method, as shown in figure 1, comprising the following steps:
s1, obtaining a remote sensing image picture of the area to be identified;
specifically, the remote sensing image picture of the soybean plot of the area to be identified is obtained from high-quality high-content remote sensing image data in the soybean growing period.
S2, inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified;
the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
Compared with the prior art, the method and the device have the advantages that the soybean plots are judged and the soybean planting area is counted based on the recognition model after deep learning, so that the statistical precision of the soybean plot planting area and the plot space positioning accuracy are effectively improved.
According to the soybean plot identification method provided by the invention, the identification model comprises a plot segmentation model, a soybean identification model and a spatial statistic model;
as shown in fig. 2, inputting the remote sensing image picture into an identification model, and outputting the identification result of the soybean plot of the area to be identified, the method comprises the following steps:
s21, inputting the remote sensing image picture into the land block segmentation model, and outputting a segmented land block image and a segmented land block vector layer in the whole area;
s22, inputting the segmented plot image into the soybean identification model, and outputting a crop category data map layer of each pixel point on the plot in the whole region;
and S23, inputting the vector map layer of the partitioned land blocks in the whole region and the crop category data map layer of each pixel point on the land blocks in the whole region into the space statistical model for superposition analysis, and judging the soybean land blocks and counting the soybean planting area according to the result of the superposition analysis.
Specifically, the identification model comprises a land parcel segmentation model, a soybean identification model and a spatial statistics model; and after a land block segmentation method and a soybean identification method are sequentially adopted for the input remote sensing image picture of the soybean land block of the area to be identified, and the obtained vector map layer of the divided land block in the whole area and the obtained crop category data map layer of each pixel point on the land block in the whole area are subjected to space superposition.
According to the soybean plot identification method provided by the invention, the segmented plot vector map layer in the whole region and the crop category data map layer of each pixel point on the plot in the whole region are input into the space statistical model for superposition analysis, and the method comprises the following steps:
and superposing layer data of corresponding coordinate points one by one on the segmented block vector layer in the whole region and the crop category data layer of each pixel point on the block in the whole region according to the geographic coordinate information.
Specifically, the superposition analysis of the vector map layer of the partitioned block in the whole region and the crop category data map layer of each pixel point on the block in the whole region is realized by using a spatial analysis module (spatial analysis tools) in the arcgis software, and the block is determined to be the soybean block when the percentage of the soybean pixel points is set to be more than 80% in the application.
According to the soybean plot identification method provided by the invention, a sample image of a soybean sampling plot comprises a remote sensing image picture, radar image data and soybean sampling plot vector data;
specifically, in the early data preparation stage, firstly field investigation is carried out to obtain soybean planting plot samples in a research area, namely soybean sampling plot vector data, and secondly, high-quality high-component remote sensing image data in the soybean growing period and time sequence radar image data in the soybean growing period are obtained.
As shown in fig. 3, the recognition model is obtained by training based on a sample image of a soybean sampling land and a corresponding recognition label, and includes:
s201, selecting a remote sensing image picture of a partial area for label processing and then taking the remote sensing image picture as a training sample image to train the land parcel segmentation model;
s202, inputting the remote sensing image picture of the whole region as a verification sample image into the trained land block segmentation model to obtain segmented land block data;
s203, training the soybean recognition model by using the radar image data, the soybean sampling plot vector data and the segmented plot data;
wherein the soybean sampled block vector data is obtained by field sampling.
According to the soybean plot identification method provided by the invention, the remote sensing image picture of the selected partial area is subjected to label processing and then used as a training sample image to train the plot segmentation model, and the method comprises the following steps:
selecting a target remote sensing image picture from the remote sensing image pictures of the whole area to edit and manufacture an identification label of a plot sample and a plot segmentation remote sensing image, and creating an identification label data set according to the identification label of the plot sample; the land block segmentation remote sensing image corresponds to the identification labels of the land block samples one by one;
specifically, the label processing method is to draw out the plot in a part of the research area by combining the manual visual interpretation of the on-site research data, and create a plot segmentation label data set as shown in fig. 4 and a corresponding plot segmentation remote sensing image data set as shown in fig. 5.
And inputting the identification label of the land sample and the land segmentation remote sensing image corresponding to the identification label into a land segmentation model so as to realize semantic segmentation training of the land segmentation model.
Specifically, the implementation of semantic segmentation training on the block segmentation model comprises: the plot labels and the corresponding remote sensing images are input and trained by a plot segmentation model of depeplab v3+, the mIoU of the model in the application of the embodiment reaches 89.8, and the OA reaches 94.8.
According to the soybean parcel identification method provided by the invention, the training of the soybean identification model by using radar image data, soybean sampling parcel vector data and the partitioned parcel data comprises the following steps:
rasterizing the vector data of the soybean sampling land blocks and the segmented land block data according to the resolution of the radar image data to obtain a raster data set;
extracting time sequence radar data corresponding to each pixel point from each raster data in the raster data set to obtain a radar data set of the soybean identification sample and a radar data set of each pixel point on the land parcel; the time sequence radar data corresponding to each pixel point comprises geographic coordinate information of the radar for spatial superposition analysis;
specifically, crop identification sample preparation is carried out before a soybean identification model is trained, a soybean plot and a segmented vector plot are resampled by combining the resolution (for example, ten meters) of radar image data, time sequence radar data corresponding to each grid pixel point are extracted, and a crop identification sample data set is manufactured and comprises a soybean identification sample radar data set and a radar data set of each pixel point on the plot.
And training a time sequence deep learning model by using the soybean identification sample radar data set to obtain the soybean identification model, and identifying and predicting the radar data set of each pixel point on the land parcel.
Specifically, the time series deep learning module is based on a long-short term memory network LSTM; and training a soybean recognition model based on LSTM, predicting pixel points on a plot in the research area, and outputting prediction results (soybean, corn or other) of the pixel points on the plot.
According to the soybean plot identification method provided by the invention, the segmented plot vector map layer in the whole region is obtained by mapping the segmented plot data and the remote sensing image picture of the whole region based on the plot segmentation model, wherein a part of samples of the segmented plot vector map layer are shown in FIG. 6 according to pixels and adding geographic coordinate information;
specifically, based on deeplab v3+ training the plot segmentation model, the entire study area is segmented and matched with geographic coordinate information, some samples of which are shown in fig. 7.
And the crop category data map layer of each pixel point on the plot in the whole region is obtained by identifying and predicting the radar data set of each pixel point on the plot based on the soybean identification model.
The following describes a soybean plot identification system provided by the present invention, and the following description and the above-described soybean plot identification method may be referred to in correspondence.
The present invention provides a soybean plot identification system 800, as shown in fig. 8, comprising a data input module 810 and an identification module 820;
the data input module 810 is configured to obtain a remote sensing image picture of a region to be identified;
the identification module 820 is configured to input the remote sensing image picture and output an identification result of the soybean plot of the area to be identified;
the identification module 820 is obtained by training based on a sample image of a soybean sampling land and a corresponding identification label, wherein the identification label is predetermined according to the soybean sampling sample and corresponds to the sample image one by one.
Specifically, when the recognition module 820 is trained, the data input module 810 receives a sample image of a soybean sampling land including soybean sampling land vector data of a research area obtained by field sampling, remote sensing image data of the research area, and radar image data; the identification module comprises a plot partitioning module 821, a soybean identification module 822 and a spatial statistic analysis module 823; wherein the plot partitioning module 821 comprises a plot partitioning model and the soybean identification module 822 comprises a soybean identification model; sending the remote sensing image data to the plot partitioning module 821, sending the radar image data and the soybean sampling plot vector data to the soybean identification module 822 at the same time, and receiving the partitioned plot data output by the plot partitioning module 821 by the soybean identification module 822; the space statistics analysis module 823 receives the map vector layer after the intra-region segmentation output by the map segmentation module 821, and simultaneously receives the crop category data layer of each pixel point on the intra-region map output by the soybean identification module 822.
The land block segmentation module 821 is configured to train a land block segmentation model by using the remote sensing image picture to obtain segmented land block data, and obtain a vector map layer of the segmented land block in the whole region according to the segmented land block data;
the soybean identification module 822 is used for training a soybean identification model according to the radar image data, the soybean sampling land block vector data and the segmented land block data to obtain a crop category data map layer of each pixel point on a land block in the whole region;
and the space statistics and analysis module 823 is configured to perform superposition analysis on the segmented region block vector map layer in the whole region and the crop category data map layer of each pixel point on the region block in the whole region, and determine a soybean region block and perform statistics on a soybean planting area according to a result of the superposition analysis.
Specifically, for example, as shown in the schematic diagram of the crop type planting area shown in fig. 9, the segmented plot image and the soybean pixel data are subjected to spatial superposition analysis, an area in which the soybean pixels are highly concentrated and are independently segmented plots is determined as a soybean planting plot, and the soybean planting area is counted. Wherein, the plots 1 and 2 are identified as the high concentration of the pixel points of the soybeans, and the plots are determined as the soybean planting areas; and similarly, the land parcels 3, 4 and 5 are corn planting areas.
Compared with the prior art, the embodiment of the invention fully utilizes the soybean sampling plot vector data of the research area obtained by on-site sampling, and the acquired remote sensing image data and radar image data of the research area, judges the soybean plot and counts the soybean planting area through the trained plot segmentation module and the soybean identification module in sequence, and effectively improves the statistical precision of the soybean plot planting area and the plot space positioning problem.
Fig. 10 illustrates a physical structure diagram of an electronic device, and as shown in fig. 10, the electronic device may include: a processor (processor)1010, a communication Interface (Communications Interface)1020, a memory (memory)1030, and a communication bus 1040, wherein the processor 1010, the communication Interface 1020, and the memory 1030 communicate with each other via the communication bus 1040. Processor 1010 may invoke logic instructions in memory 1030 to perform a soybean parcel identification method comprising: obtaining a remote sensing image picture of a soybean plot of an area to be identified; inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified; the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
Furthermore, the logic instructions in the memory 1030 can be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method for identifying soybean blocks provided by the above methods, the method comprising: acquiring a remote sensing image picture of a region to be identified; inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified; the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the soybean parcel identification method provided above, the method comprising: acquiring a remote sensing image picture of a region to be identified; inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified; the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units 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. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A soybean plot identification method is characterized by comprising the following steps:
acquiring a remote sensing image picture of a region to be identified;
inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified;
the identification model is obtained after training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
2. The soybean plot identification method of claim 1, wherein the identification model comprises a plot segmentation model, a soybean identification model, and a spatial statistics model;
inputting the remote sensing image picture into an identification model, and outputting an identification result of the soybean plot of the area to be identified, wherein the identification result comprises the following steps:
inputting the remote sensing image picture into the land block segmentation model, and outputting a segmented land block image and a segmented land block vector layer in the whole region;
inputting the segmented plot images into the soybean identification model, and outputting the crop category data map layers of all pixel points on the plots in the whole region;
inputting the segmented plot vector layer in the whole region and the crop category data layer of each pixel point on the plot in the whole region into the space statistical model for superposition analysis, and judging the soybean plot and counting the soybean planting area according to the result of the superposition analysis.
3. The soybean plot identification method according to claim 2, wherein inputting the segmented plot vector map layer in the whole region and the crop category data map layer of each pixel point on the plot in the whole region into the spatial statistical model for superposition analysis comprises:
and superposing layer data of corresponding coordinate points one by one on the segmented block vector layer in the whole region and the crop category data layer of each pixel point on the block in the whole region according to the geographic coordinate information.
4. The soybean block recognition method according to claim 1 or 2, wherein the sample image of the soybean sampled block includes a remote sensing image picture, radar image data, and soybean sampled block vector data;
the identification model is obtained after training based on a sample image of a soybean sampling land and a corresponding identification label, and comprises the following steps:
selecting a remote sensing image picture of a partial area for label processing and then taking the remote sensing image picture as a training sample image to train the land parcel segmentation model;
inputting the remote sensing image picture of the whole area as a verification sample image into the trained plot segmentation model to obtain the data of the segmented plot;
training the soybean recognition model by using the radar image data, the soybean sampling plot vector data and the segmented plot data;
wherein the soybean sampled block vector data is obtained by field sampling.
5. The soybean plot recognition method of claim 4, wherein the selecting of the remote sensing image picture of the partial region for label processing and then training of the plot segmentation model as a training sample image comprises:
selecting a target remote sensing image picture from the remote sensing image pictures of the whole area to edit and manufacture an identification label of a plot sample and a plot segmentation remote sensing image, and creating an identification label data set according to the identification label of the plot sample; the land block segmentation remote sensing image corresponds to the identification labels of the land block samples one by one;
and inputting the identification label of the land sample and the land segmentation remote sensing image corresponding to the identification label into a land segmentation model so as to realize semantic segmentation training of the land segmentation model.
6. The soybean patch recognition method of claim 4, wherein the training the soybean recognition model using the radar image data, the soybean sampled patch vector data, and the segmented patch data comprises:
rasterizing the vector data of the soybean sampling land blocks and the segmented land block data according to the resolution of the radar image data to obtain a raster data set;
extracting time sequence radar data corresponding to each pixel point from each raster data in the raster data set to obtain a radar data set of the soybean identification sample and a radar data set of each pixel point on the land parcel; the time sequence radar data corresponding to each pixel point comprises geographic coordinate information of the radar for spatial superposition analysis;
and training a time sequence deep learning model by using the soybean identification sample radar data set to obtain the soybean identification model, and identifying and predicting the radar data set of each pixel point on the land parcel.
7. The soybean plot identification method according to claim 6, wherein the segmented plot vector map layer in the whole region is obtained by mapping the segmented plot data with the remote sensing image picture of the whole region according to pixels and adding geographical coordinate information based on the plot segmentation model;
and the crop category data map layer of each pixel point on the plot in the whole region is obtained by identifying and predicting the radar data set of each pixel point on the plot based on the soybean identification model.
8. A soybean plot identification system is characterized by comprising a data input module and an identification module;
the data input module is used for acquiring a remote sensing image picture of a region to be identified;
the identification module is used for inputting the remote sensing image picture and outputting an identification result of the soybean plot of the area to be identified;
the identification module is obtained by training based on a sample image of a soybean sampling land and corresponding identification labels, and the identification labels are predetermined according to the soybean sampling land and correspond to the sample image one by one.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of identifying soybean patches of any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method for identifying soybean blocks as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110060578.6A CN112966548B (en) | 2021-01-18 | 2021-01-18 | Soybean plot identification method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110060578.6A CN112966548B (en) | 2021-01-18 | 2021-01-18 | Soybean plot identification method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112966548A true CN112966548A (en) | 2021-06-15 |
| CN112966548B CN112966548B (en) | 2023-10-27 |
Family
ID=76271547
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110060578.6A Active CN112966548B (en) | 2021-01-18 | 2021-01-18 | Soybean plot identification method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112966548B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657158A (en) * | 2021-07-13 | 2021-11-16 | 西安电子科技大学 | Google Earth Engine-based large-scale soybean planting region extraction algorithm |
| CN113763396A (en) * | 2021-09-02 | 2021-12-07 | 中国农业科学院农业信息研究所 | Remote sensing image plot extraction method and system based on deep learning |
| CN113780057A (en) * | 2021-07-23 | 2021-12-10 | 贵州图智信息技术有限公司 | Idle land identification method and device |
| CN114529097A (en) * | 2022-02-26 | 2022-05-24 | 黑龙江八一农垦大学 | Multi-scale crop phenological period remote sensing dimensionality reduction prediction method |
| CN115082808A (en) * | 2022-06-17 | 2022-09-20 | 安徽大学 | Soybean planting region extraction method based on high-score first data and U-Net model |
| CN115205691A (en) * | 2022-09-15 | 2022-10-18 | 江西农业大学 | Rice planting area identification method and device, storage medium and equipment |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800113A (en) * | 2012-07-18 | 2012-11-28 | 合肥工业大学 | Digital image analysis method based on fractal dimension |
| CN103544502A (en) * | 2013-10-29 | 2014-01-29 | 上海市城市建设设计研究总院 | High-resolution remote-sensing image ship extraction method based on SVM |
| CN103714562A (en) * | 2013-12-26 | 2014-04-09 | 广东威创视讯科技股份有限公司 | Method and device for determining drawing point in complex area |
| CN104915636A (en) * | 2015-04-15 | 2015-09-16 | 北京工业大学 | Remote sensing image road identification method based on multistage frame significant characteristics |
| CN104931022A (en) * | 2015-04-21 | 2015-09-23 | 国家测绘地理信息局卫星测绘应用中心 | Satellite image three-dimensional area network adjustment method based on satellite-borne laser height measurement data |
| CN108335261A (en) * | 2018-02-08 | 2018-07-27 | 重庆市地理信息中心 | A kind of Optical remote satellite orthography garland region automatic testing method |
| CN109543630A (en) * | 2018-11-28 | 2019-03-29 | 苏州中科天启遥感科技有限公司 | Remote sensing image forest land extracting method and system, storage medium, electronic equipment based on deep learning |
| CN109840553A (en) * | 2019-01-17 | 2019-06-04 | 苏州中科天启遥感科技有限公司 | The extracting method and system, storage medium, electronic equipment for agrotype of ploughing |
| CN110008854A (en) * | 2019-03-18 | 2019-07-12 | 中交第二公路勘察设计研究院有限公司 | Unmanned plane image Highway Geological Disaster recognition methods based on pre-training DCNN |
| CN110598503A (en) * | 2018-06-12 | 2019-12-20 | 何雪梅 | Remote sensing image recognition algorithm of neural network |
| CN111414878A (en) * | 2020-03-26 | 2020-07-14 | 京东城市(北京)数字科技有限公司 | Method and device for social attribute analysis and image processing of land parcel |
| CN111476197A (en) * | 2020-04-24 | 2020-07-31 | 中科天盛卫星技术服务有限公司 | Oil palm identification and area extraction method and system based on multi-source satellite remote sensing image |
| CN111768337A (en) * | 2020-06-01 | 2020-10-13 | 中国科学院空天信息创新研究院 | Image processing method and device and electronic equipment |
| CN112101309A (en) * | 2020-11-12 | 2020-12-18 | 北京道达天际科技有限公司 | Ground object target identification method and device based on deep learning segmentation network |
-
2021
- 2021-01-18 CN CN202110060578.6A patent/CN112966548B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800113A (en) * | 2012-07-18 | 2012-11-28 | 合肥工业大学 | Digital image analysis method based on fractal dimension |
| CN103544502A (en) * | 2013-10-29 | 2014-01-29 | 上海市城市建设设计研究总院 | High-resolution remote-sensing image ship extraction method based on SVM |
| CN103714562A (en) * | 2013-12-26 | 2014-04-09 | 广东威创视讯科技股份有限公司 | Method and device for determining drawing point in complex area |
| CN104915636A (en) * | 2015-04-15 | 2015-09-16 | 北京工业大学 | Remote sensing image road identification method based on multistage frame significant characteristics |
| CN104931022A (en) * | 2015-04-21 | 2015-09-23 | 国家测绘地理信息局卫星测绘应用中心 | Satellite image three-dimensional area network adjustment method based on satellite-borne laser height measurement data |
| CN108335261A (en) * | 2018-02-08 | 2018-07-27 | 重庆市地理信息中心 | A kind of Optical remote satellite orthography garland region automatic testing method |
| CN110598503A (en) * | 2018-06-12 | 2019-12-20 | 何雪梅 | Remote sensing image recognition algorithm of neural network |
| CN109543630A (en) * | 2018-11-28 | 2019-03-29 | 苏州中科天启遥感科技有限公司 | Remote sensing image forest land extracting method and system, storage medium, electronic equipment based on deep learning |
| CN109840553A (en) * | 2019-01-17 | 2019-06-04 | 苏州中科天启遥感科技有限公司 | The extracting method and system, storage medium, electronic equipment for agrotype of ploughing |
| CN110008854A (en) * | 2019-03-18 | 2019-07-12 | 中交第二公路勘察设计研究院有限公司 | Unmanned plane image Highway Geological Disaster recognition methods based on pre-training DCNN |
| CN111414878A (en) * | 2020-03-26 | 2020-07-14 | 京东城市(北京)数字科技有限公司 | Method and device for social attribute analysis and image processing of land parcel |
| CN111476197A (en) * | 2020-04-24 | 2020-07-31 | 中科天盛卫星技术服务有限公司 | Oil palm identification and area extraction method and system based on multi-source satellite remote sensing image |
| CN111768337A (en) * | 2020-06-01 | 2020-10-13 | 中国科学院空天信息创新研究院 | Image processing method and device and electronic equipment |
| CN112101309A (en) * | 2020-11-12 | 2020-12-18 | 北京道达天际科技有限公司 | Ground object target identification method and device based on deep learning segmentation network |
Non-Patent Citations (5)
| Title |
|---|
| WILLYAN RONALDO BECKER 等: "Harvest date forecast for soybeans from maximum vegetative development using satellite images", 《INTERNATIONAL JOURNAL OF REMOTE SENSING》, pages 1121 - 1138 * |
| 杨闫君: "基于植被指数时序谱类内差异特征的冬小麦遥感识别研究", 《中国博士论文全文数据库 农业科技辑》, pages 043 - 3 * |
| 王顺: "基于轻量和积网络的田间杂草识别", 《中国优秀硕士学位论文全文数据库 农业科技辑》, pages 046 - 341 * |
| 陈爱莲 等: "卫星遥感估产技术在大豆区域收入保险中的应用", 《智慧农业》, vol. 2, no. 03, pages 139 - 152 * |
| 黄文江 等: "基于遥感技术的作物病虫害监测研究进展", 《农业工程技术》, pages 39 - 45 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657158A (en) * | 2021-07-13 | 2021-11-16 | 西安电子科技大学 | Google Earth Engine-based large-scale soybean planting region extraction algorithm |
| CN113780057A (en) * | 2021-07-23 | 2021-12-10 | 贵州图智信息技术有限公司 | Idle land identification method and device |
| CN113763396A (en) * | 2021-09-02 | 2021-12-07 | 中国农业科学院农业信息研究所 | Remote sensing image plot extraction method and system based on deep learning |
| CN113763396B (en) * | 2021-09-02 | 2023-07-25 | 中国农业科学院农业信息研究所 | Remote sensing image land parcel extraction method and system based on deep learning |
| CN114529097A (en) * | 2022-02-26 | 2022-05-24 | 黑龙江八一农垦大学 | Multi-scale crop phenological period remote sensing dimensionality reduction prediction method |
| CN115082808A (en) * | 2022-06-17 | 2022-09-20 | 安徽大学 | Soybean planting region extraction method based on high-score first data and U-Net model |
| CN115205691A (en) * | 2022-09-15 | 2022-10-18 | 江西农业大学 | Rice planting area identification method and device, storage medium and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112966548B (en) | 2023-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112966548B (en) | Soybean plot identification method and system | |
| CN111986099B (en) | Tillage monitoring method and system based on convolutional neural network with residual error correction fused | |
| Giada et al. | Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania | |
| CN111626947B (en) | Map vectorization sample enhancement method and system based on generation of countermeasure network | |
| US20220215656A1 (en) | Method, apparatus, device for image processing, and storage medium | |
| CN110363176B (en) | Image analysis method and device | |
| CN112419202B (en) | Automatic wild animal image recognition system based on big data and deep learning | |
| CN112989995B (en) | Text detection method and device and electronic equipment | |
| CN112883900B (en) | Method and device for bare-ground inversion of visible images of remote sensing images | |
| Ye et al. | Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model | |
| CN113128413A (en) | Face detection model training method, face detection method and related device thereof | |
| CN115375548A (en) | Super-resolution remote sensing image generation method, system, equipment and medium | |
| CN115512238A (en) | Method and device for determining damaged area, storage medium and electronic device | |
| CN113033386B (en) | High-resolution remote sensing image-based transmission line channel hidden danger identification method and system | |
| CN110751163B (en) | Target positioning method and device, computer readable storage medium and electronic equipment | |
| CN116758419A (en) | Multi-scale target detection method, device and equipment for remote sensing image | |
| CN116740135A (en) | Infrared dim target tracking method and device, electronic equipment and storage medium | |
| CN116704324A (en) | Target detection method, system, equipment and storage medium based on underwater image | |
| CN111104965A (en) | Vehicle target identification method and device | |
| CN116246161A (en) | Method and device for identifying target fine type of remote sensing image under guidance of domain knowledge | |
| CN117437654B (en) | Semantic recognition-based grid map analysis method, device and medium | |
| CN116385818B (en) | Training method, device and equipment of cloud detection model | |
| CN113034420A (en) | Industrial product surface defect segmentation method and system based on frequency space domain characteristics | |
| CN116109478B (en) | Remote sensing image mosaic processing method and device based on comparability | |
| CN110852145B (en) | Image detection method, device and system for unmanned aerial vehicle image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |