CN116469016A - Vegetation state monitoring method and device based on abundance value and storage medium - Google Patents
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Abstract
The application discloses a vegetation state monitoring method, device and storage medium based on abundance values. The method comprises the following steps: determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal; determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel; determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value; determining a vegetation region according to the second pixel; and determining vegetation change state information in a predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
Description
Technical Field
The application relates to the technical field of satellite remote sensing, in particular to a vegetation state monitoring method, device and storage medium based on abundance values.
Background
Satellite remote sensing technology has been widely used for monitoring earth conditions. For example, changes in the vegetation area, such as changes in the area of the vegetation area, etc., may be analyzed by the remote sensing image. To make statistics on the area change of the vegetation region, it is necessary to accurately determine the edges of the vegetation region.
In the prior art, the edge of a vegetation area is usually extracted from a remote sensing image through an image recognition model or an edge extraction model such as a neural network, so as to determine the area of the vegetation area. However, this identification method can only accurately make a judgment if the edges of the vegetation area are clear.
But in general the edges of the vegetation area are always made up of mixed picture elements comprising a plurality of end members. The edges of the vegetation area are often unclear. It is therefore difficult to accurately extract the edges of the vegetation region if the vegetation region is identified by means of an image, so that the variation of the vegetation region cannot be accurately estimated.
Aiming at the technical problems that the fuzzy edge of the vegetation area can not be accurately identified by the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the disclosure provides a vegetation state monitoring method, device and storage medium based on an abundance value, which at least solve the technical problem that in the prior art, a fuzzy edge of a vegetation region cannot be accurately identified in an image identification mode, and further the change of the vegetation region cannot be accurately estimated.
According to an aspect of the embodiments of the present disclosure, there is provided a vegetation state monitoring method based on an abundance value, including: determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal; determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel; determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value; determining the vegetation region according to the second pixel; and determining vegetation change state information in the predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method described above is performed by a processor when the program is run.
According to another aspect of the embodiments of the present disclosure, there is also provided a vegetation state monitoring device based on an abundance value, including: the first pixel determining module is used for determining a plurality of first pixels corresponding to a preset area on the ground according to a satellite remote sensing reflected image generated based on the ground reflection spectrum signal; the vegetation abundance value determining module is used for determining the vegetation abundance value of the end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel; a second pixel determining module, configured to determine a second pixel corresponding to an edge of a vegetation region in the predetermined region according to the determined vegetation abundance value; a vegetation region determining module, configured to determine the vegetation region according to the second pixel; and a vegetation change state determining module for determining vegetation change state information in the predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
According to another aspect of the embodiments of the present disclosure, there is also provided a vegetation state monitoring device based on an abundance value, including: a processor; and a memory, coupled to the processor, for providing instructions to the processor to process the steps of: determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal; determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel; determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value; determining the vegetation region according to the second pixel; and determining vegetation change state information in the predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
In the embodiment of the disclosure, the edge of the vegetation region is not identified by an image identification method, but by analyzing the abundance value of the green vegetation end member in each pixel, and determining the edge of the vegetation region according to the abundance value. Therefore, compared with an image recognition mode, the method and the device can extract the edge of the vegetation image area based on the mixed pixels of the reflected remote sensing image more accurately, and further accurately determine the vegetation area in the preset area, so that the change of the vegetation area is estimated accurately. The method solves the technical problems that the fuzzy edge of the vegetation area can not be accurately identified in the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and do not constitute an undue limitation on the disclosure. In the drawings:
FIG. 1 is a block diagram of a hardware architecture of a computing device for implementing a method according to embodiment 1 of the present disclosure;
FIG. 2 is a flow chart of a vegetation state monitoring method based on abundance values according to embodiment 1 of the disclosure;
fig. 3 is a schematic view of a predetermined area as a monitoring object according to embodiment 1 of the present disclosure;
FIG. 4 is a schematic view of a satellite remote sensing reflection image corresponding to a predetermined area according to embodiment 1 of the present disclosure;
FIG. 5 is a schematic view of a ground area corresponding to a pixel according to embodiment 1 of the disclosure;
FIG. 6 is a schematic diagram of an abundance-based vegetation-status monitoring device according to embodiment 2 of the present disclosure; and
fig. 7 is a schematic diagram of an abundance-based vegetation-status monitoring device according to embodiment 3 of the present disclosure.
Detailed Description
In order to better understand the technical solutions of the present disclosure, the following description will clearly and completely describe the technical solutions of the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. It will be apparent that the described embodiments are merely embodiments of a portion, but not all, of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure, shall fall within the scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to the present embodiment, there is provided a method embodiment of a vegetation status monitoring method based on abundance values, it should be noted that the steps illustrated in the flowchart of the drawing may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that herein.
The method embodiments provided by the present embodiments may be performed in a mobile terminal, a computer terminal, a server, or similar computing device. FIG. 1 illustrates a hardware block diagram of a computing device for implementing an abundance-based vegetation status monitoring method. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a microprocessor MCU, a processing device such as a programmable logic device FPGA), memory for storing data, transmission means for communication functions, and input/output interfaces. Wherein the memory, the transmission device and the input/output interface are connected with the processor through a bus. In addition, the method may further include: a display connected to the input/output interface, a keyboard, and a cursor control device. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the embodiments of the present disclosure, the data processing circuit acts as a processor control (e.g., selection of the variable resistance termination path to interface with).
The memory may be used to store software programs and modules of application software, such as a program instruction/data storage device corresponding to the vegetation state monitoring method based on abundance values in the embodiments of the disclosure, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the vegetation state monitoring method based on abundance values of the application program. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to the computing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communications provider of the computing device. In one example, the transmission means comprises a network adapter (Network Interface Controller, NIC) connectable to other network devices via the base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.
It should be noted herein that in some alternative embodiments, the computing device shown in FIG. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computing devices described above.
In the above-described operating environment, according to a first aspect of the present embodiment, there is provided a vegetation status monitoring method based on an abundance value, the method being implemented by the computing device shown in fig. 1. Fig. 2 shows a schematic flow chart of the method, and referring to fig. 2, the method includes:
s202: determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal;
s204: determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel;
S206: determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value;
s208: determining a vegetation region according to the second pixel; and
s210: and determining vegetation change state information in a predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area which is preset in the predetermined area and used for planting vegetation.
Specifically, fig. 3 shows a schematic view of a predetermined area 100 as a monitoring object, the predetermined area 100 being, for example, a predetermined area on the ground and including a vegetation area 110. Further, fig. 4 shows a schematic diagram of a satellite remote sensing reflection image 200 corresponding to the predetermined area 100 acquired from a satellite.
A schematic view of a satellite remote sensing reflection image 200 corresponding to a predetermined area on the ground is acquired by the computing device, and the satellite remote sensing reflection image 200 includes a vegetation image area 210 corresponding to the vegetation area 110. And referring to fig. 4, a satellite remote sensing reflected image 200 includes a plurality of picture elements 201 (i.e., the first picture elements of the claims).
The satellite remote sensing reflection image 200 may be obtained by, for example, a surface reflectivity sensor on a satellite. For example, an OLI sensor of land at 8 may be provided on the satellite, so that satellite remote sensing reflection image 200 of predetermined area 100 may be acquired by the OLI sensor of land at 8. The computing device shown in fig. 1 may thus acquire surface reflectivity data for a plurality of different frequency bands for each pixel. For example, the OLI sensor of lansat 8 may provide surface reflectance data for 9 different bands, so that the computing device may acquire surface reflectance data for 9 different bands for each pixel. So that the computing device can determine a plurality of pixels 201 (i.e., first pixels) corresponding to the predetermined area 100 from the satellite remote sensing reflected image 200 (S202).
Wherein the picture elements 201 in the remote sensing reflectance image 200 include both end-members (i.e., clear picture elements) and blended picture elements. Wherein an end member refers to a picture element consisting of only one ground object type. For example, in this embodiment, the different types of features include: green vegetation, building, empty land, agricultural land, water and other 5 land types. Of course, those skilled in the art may also specify more types of features, and will not be described in detail herein. Thus, for example, the end members may comprise only one type of land feature, such as green vegetation, or only one type of land feature, such as construction, and will not be described in detail herein.
The mixed pixel is a pixel comprising a plurality of different ground object types, for example, the pixel comprises two ground object types of green vegetation and open land, so the mixed pixel can be regarded as a mixed pixel of the green vegetation and the open land. In the mixed pixel, the end members of different ground object types occupy different areas and can be represented by abundance values of the end members.
Then, further, for each of the picture elements 201 shown in fig. 4, the computing device calculates the abundance value of the green vegetation end member in that picture element from the spectral features corresponding to the different frequency bands. That is, the area occupied by the green vegetation in the ground area corresponding to the pixel is calculated (S204).
The spectral features may be, for example, reflectance data of different frequency bands corresponding to the pixel obtained by a surface reflectance sensor, or reflectance data of a plurality of spectrums corresponding to the pixel obtained after principal component analysis. The computing device can thereby determine the abundance values of the green vegetation end in each pixel based on the spectral characteristics of each pixel. The method of determining the abundance of the green vegetation end in each pixel will be described in detail below and will not be described in detail here.
The computing device then determines a pixel 201c (i.e., a second pixel) corresponding to the edge of the vegetation image area 210 based on the abundance values of the green vegetation end in the individual pixels of the satellite remote sensing reflectance image 200. In particular, according to the present embodiment, the abundance values of the green vegetation end members are also different in the pixels of the different image areas. For example, for pixel 201a in the vegetation image area, the abundance value of the green vegetation end would be near 100%, and for pixel 201b in the non-vegetation image area, the abundance value of the green vegetation end would be very low, even near 0. Thus, the pixel 201c corresponding to the edge of the vegetation image area 210 can be determined according to the abundance value of the green vegetation end member in the pixel 201 in the satellite remote sensing reflection image 200. For example, since the pixel 201c corresponding to the edge of the vegetation image area is likely to be a hybrid pixel containing a portion of the green vegetation end member, the pixel 201c corresponding to the edge of the vegetation image area (i.e., the second pixel) can be determined from the pixels having an abundance value of the green vegetation end member below the predetermined threshold.
Then, the computing device further determines a vegetation image area 210 surrounded by the pixel 201c according to the determined pixel 201c, thereby further determining a vegetation area 110 corresponding to the vegetation image area (S208).
Further, the computing device then compares the determined vegetation region 110 to the pre-set reference region information. The reference area information may be, for example, land planning information made by the relevant departments, in which information such as the position, shape, area, etc. of a vegetation area in which vegetation should be planted in the predetermined area is specified. So that by comparison, the computing device can determine vegetation change status information of the actual vegetation region 110 relative to the reference region information (S210).
The vegetation change status information includes (but is not limited to): change information of the vegetation area (for indicating whether the area of the vegetation area is increased or decreased), information of the vegetation degradation area (for indicating the area where vegetation is degraded), information of the vegetation extension area (for indicating the extended vegetation area), and the like. Therefore, the vegetation area can be monitored through the information.
As mentioned in the background art, the edge of a vegetation area is usually always made up of a hybrid picture element comprising a plurality of end members. The edges of the vegetation area are often unclear. It is therefore difficult to accurately extract the edges of the vegetation region if the vegetation region is identified by means of an image, so that the variation of the vegetation region cannot be accurately estimated.
In view of this, the technical solution proposed in the present disclosure does not identify the edge of the vegetation region by means of image recognition, but by analyzing the abundance values of the green vegetation end members in the respective pixels, and determining the edge of the vegetation region according to the abundance values. Therefore, compared with an image recognition mode, the method and the device can extract the edge of the vegetation image area based on the mixed pixels of the reflected remote sensing image more accurately, and further accurately determine the vegetation area in the preset area, so that the change of the vegetation area is estimated accurately. The method solves the technical problems that the fuzzy edge of the vegetation area can not be accurately identified in the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated.
Optionally, the operation of determining the vegetation abundance value of the end member corresponding to the vegetation ground type in the first pixel according to the spectral feature corresponding to the first pixel comprises: acquiring reference spectrum characteristics corresponding to end members of different ground object types; and determining abundance values of the end members of different ground object types in the first pixel according to the reference spectrum characteristic and the spectrum characteristic corresponding to the first pixel.
In particular, the computing device is in the process of determining vegetation abundance values with the green vegetation end members in the individual pixels 201 of the satellite remote sensing reflectance image 200. The abundance value of an end member corresponding to a different terrain type in each pixel 201 is determined for that pixel 201 by.
First, for a pel 201, the computing device obtains a spectral feature Fd corresponding to that pel 201:
Fd＝[fd 1 ,fd 2 ,...,fd m ] T
wherein fd is i (i=1 to m) is reflectance data of different frequency bands corresponding to the pixel 201. The different frequency bands may be, for example, reflectance data of different frequency bands generated by a surface reflectance sensor, or reflectance data of several frequency bands obtained after principal component analysis.
Since in this embodiment the variables to be determined include the abundance values corresponding to the end members of the 5 different ground object types, and an error term. Thus m.gtoreq.6, i.e. the number of parameters in the spectral signature is at least 1 more than the number of end members of different ground object types. In addition, in the following, each frequency band corresponding to the reference spectrum characteristic corresponding to the end member of the different feature type is the same as the frequency band in the spectrum characteristic of the pixel 201. For example, the reference spectral features corresponding to the different feature type end members also include m frequency bands and are the same as the frequency bands of the spectral features of pixel 201. And will not be described in detail herein.
Then, the computing device obtains reference spectral features corresponding to the end members of the different surface feature types, respectively. The reference spectral feature may be predetermined, for example, by measurement. In this embodiment, the reference spectrum features corresponding to the end members of different feature types include, for example:
Reference spectral features F of green vegetation end corresponding to vegetation ground type 1 ：
F 1 ＝[f 11 ,f 12 ,...,f 1m ]Wherein f 1i (i=1 to m) is the reflectivity data of different frequency bands corresponding to the green vegetation end members.
Reference spectral features F of building end members corresponding to types of building features 2 ：
F 2 ＝[f 21 ,f 22 ,...,f 2m ]Wherein f 2i (i=1 to m) is the reflectivity data of different frequency bands corresponding to the building end members.
Reference spectral features F of air-to-ground end members corresponding to air-to-ground object types 3 ：
F 3 ＝[f 31 ,f 32 ,...,f 3m ]Wherein f 3i (i=1 to m) is the reflectance data of different frequency bands corresponding to the air-ground end members.
Reference spectral features F of agricultural land end members corresponding to the type of agricultural land feature 4 ：
F 4 ＝[f 41 ,f 42 ,...,f 4m ]Wherein f 4i (i=1 to m) is the reflectivity data of different frequency bands corresponding to the agricultural land end members.
Reference spectrum characteristic F of water body end member corresponding to water body ground object type 5 ：
F 5 ＝[f 51 ,f 52 ,...,f 5m ]Wherein f 5i And (i=1-m) is the reflectivity data of different frequency bands corresponding to the water body end members.
The computing device then builds an equation according to the following formula:
k 0 +k 1 F 1 +k 2 F 2 +k 3 F 3 +k 4 F 4 +k 5 F 5 ＝Fd (1)
wherein k is 1 The abundance value of the green vegetation end member in the first pixel; k (k) 2 An abundance value of the building end member in the first pixel; k (k) 3 Is the abundance value of the air-ground end member in the first pixel; k (k) 4 The abundance value of the agricultural land end member in the first pixel; k (k) 5 The abundance value of the water body end member in the first pixel; k 0 Is the error term to be determined.
Thus, the computing device compares the spectral features Fd of pixel 201, the reference spectral features F of the green vegetation end members 1 Reference spectral features F of building end members 2 Reference spectral features F of air-to-ground end members 3 Reference spectral features F of agricultural land end members 4 Reference spectral features F of water body end members 5 Substituting the above formula (1) to obtain k 0 ～k 5 . Further, an abundance value k of the end member corresponding to the vegetation ground type in the pixel 201 is obtained 1 。
In this way, the computing device thus calculates the abundance values of the end members of different ground object types for each of the pixels 201 in the satellite remote sensing reflectance image 200, so that the vegetation abundance values in each of the pixels 201 can be determined as compared to the green-planted end members.
Optionally, determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value, including: determining a third pixel corresponding to the vegetation region and a fourth pixel corresponding to the non-vegetation region in the first pixel according to the vegetation abundance value; and determining the second pixel according to the third pixel and the fourth pixel.
Specifically, after determining the vegetation abundance values of the green vegetation end in each of the pixels 201 (i.e., the first pixel), the computing device may determine, in each of the pixels 201, a pixel 201a (i.e., the third pixel) corresponding to the vegetation region and a pixel 201b (i.e., the fourth pixel) corresponding to the non-vegetation region based on the vegetation abundance values of each of the pixels 201.
The computing device then determines, from the determined pels 201a and pels 201b, pels 201c corresponding to the edge of the vegetation image area 210. For example, the computing device may determine pixel 201a immediately adjacent to pixel 201b as pixel 201c. Alternatively, the computing device may also determine pixel 201b immediately adjacent to pixel 201a as pixel 201c. In this manner, the computing device is thus able to accurately determine the vegetation image area 210 corresponding to the vegetation area 110 from the vegetation abundance values of the individual pixels 201.
Optionally, determining, from the vegetation abundance values, the third pixel corresponding to the vegetation region and the fourth pixel corresponding to the non-vegetation region in the first pixel, including: and under the condition that the vegetation abundance value of the first pixel is larger than the abundance value of the end members of other ground object types, judging that the first pixel is a third pixel.
In particular, fig. 5 shows a schematic view of a ground area 101 of one pixel 201. Referring to fig. 5, when the abundance value of vegetation in an image element 201 is greater than the abundance value of the end members of other ground object types (e.g., building end members, empty end members, agricultural ground end members, and water end members), that is, when the abundance value of green vegetation end members in the image element 201 is maximum relative to the end members of other ground object types, it means that in the ground area 101 corresponding to the image element 201, green vegetation occupies the position of the main body relative to the other end members, thereby determining the image element 201 as an image element corresponding to the vegetation area. In this way, it is thus possible to accurately determine the pixel corresponding to the vegetation region in the pixel 201.
Optionally, the operation of determining the third pixel corresponding to the vegetation area and the fourth pixel corresponding to the non-vegetation area in the first pixel according to the vegetation abundance value further includes: and under the condition that the end member with the largest abundance value in the first pixel is a building end member, an agricultural land end member or a water body end member, judging that the first pixel is a fourth pixel.
In particular, the computing device determines that the vegetation abundance value of a pixel 201 is not the largest relative to the abundance values of the end members of other ground object types (e.g., building end members, empty ground end members, agricultural ground end members, and water end members), which in this case means that the green vegetation end member does not occupy the dominant position in that pixel 201. The computing device needs to further determine the end members that take the dominant position in this picture element 201.
When the computing device determines that the end member of the pixel 201 that occupies the dominant position (i.e., the end member with the greatest abundance value) is the building end member agricultural land end member or the water body end member, it indicates that the type of the ground object of the area corresponding to the pixel 201 has changed to a type of ground object different from the green vegetation. In this case, the computing device may determine that the ground area corresponding to this pixel 201 is not a vegetation area, and thus this pixel 201 is also a pixel 201b of a non-vegetation area.
Optionally, the operation of determining the third pixel corresponding to the vegetation area and the fourth pixel corresponding to the non-vegetation area in the first pixel according to the vegetation abundance value further includes: acquiring historical statistical information related to the vegetation abundance value of the first pixel under the condition that the end member with the largest abundance value in the first pixel is an air-ground end member; and determining the first pixel as a third pixel or a fourth pixel according to the historical statistical information.
Specifically, in the case where the computing device determines that, in a certain pixel 201 in the satellite remote sensing reflection image 200, the end member with the largest abundance value is the space end member, that is, in the case where the area occupied by the space is the largest proportion in the ground area corresponding to the pixel 201. To avoid false positives, the computing device may obtain historical statistics related to the vegetation abundance value corresponding to that pel 201.
For example, the computing device may obtain vegetation abundance values corresponding to that pel 201 for each month in the past. Therefore, whether the vegetation abundance value of the current pixel 201 is normal or not can be judged according to the change periodicity and the seasonality of the vegetation abundance value of the pixel 201, if the vegetation abundance value of the current pixel 201 is normal, the pixel 201 is still judged to be a third pixel corresponding to a vegetation area, otherwise, the pixel 201 is judged to be a fourth pixel corresponding to a non-vegetation area. For example, if the computing device based on historical statistics, it may determine a month in which the vegetation abundance value of pel 201 was relatively close (e.g., less than a predetermined threshold) to the currently calculated vegetation abundance value over a past predetermined period. If the determined months appear periodic throughout the year (e.g., all corresponding months throughout the year), it is indicated that the ground area corresponding to this pixel 201 is still the area corresponding to the vegetation area. If the determined month does not exhibit periodicity throughout the year (e.g., the most recent consecutive months of vegetation abundance values are all relatively close to the currently calculated vegetation abundance values), then this pixel 201 is indicated as having been transitioned to a non-vegetation region.
Furthermore, optionally, in the case where the end member with the largest abundance value in the first pixel is an air-ground end member, the computing device may also acquire historical statistical information related to the spectral feature of the first pixel; and determining the first pixel as a third pixel or a fourth pixel according to the historical statistical information.
In particular, the computing device may obtain historical statistics corresponding to spectral features of pel 201. For example, the computing device may obtain spectral features of pel 201 for each month over a predetermined period of time.
The computing device then calculates correlations between the spectral features of the past months of pel 201 and the currently calculated spectral features.
For example, asAs described above, the spectral characteristic of the current month of the pixel 201 is fd= [ Fd ] 1 ,fd 2 ,...,fd m ] T 。
Then, the spectral characteristics of pixel 201 at each month in the past were taken as Fd 1 ～Fd n . Wherein Fd 1 ＝[fd 1,1 ,fd 1,2 ,...,fd 1,m ] T ；Fd 2 ＝[fd 2,1 ,fd 2,2 ,...,fd 2,m ] T ；...；Fd n ＝[fd n,1 ,fd n,2 ,...,fd n,m ] T 。
Wherein fd is 1,i (i=1 to m) is reflectance data of the pixel 201 in different frequency bands corresponding to the 1 st month in the past. fd (fd) 2,i (i=1 to m) is the reflectance data of the pixel 201 in the different frequency bands corresponding to the 2 nd month in the past. Similarly, fd n,i (i=1 to m) is reflectance data of the pixel 201 in different frequency bands corresponding to the nth month in the past.
The computing device then calculates a correlation R between the spectral features of the past months and the spectral features calculated for the current month 1 ～R n . Wherein the correlation calculation can refer to the existing correlation calculation formula:
thus, according to the above formula, the correlation between the spectral features of the pixel 201 in the past respective months and the spectral features of the current month can be calculated.
The computing device may then select a month having a correlation greater than a predetermined threshold as a month having a higher correlation of the spectral features of pel 201 with the spectral features of the current month. The computing device then determines whether pel 201 is a pel corresponding to a vegetation region based on the determined periodicity of the higher correlation month.
For example, if the determined months exhibit periodicity throughout the year (e.g., all corresponding months throughout the year), it is indicated that the ground area corresponding to this pixel 201 is still the area corresponding to the vegetation area. If the determined month does not exhibit periodicity throughout the year (e.g., the most recent consecutive months of vegetation abundance values are all relatively close to the currently calculated vegetation abundance values), then this pixel 201 is indicated as having been transitioned to a non-vegetation region.
Thus, by the above manner, it is possible to effectively avoid erroneously determining the pixel corresponding to the vegetation region as the pixel corresponding to the non-vegetation region due to seasonal variation of vegetation. Therefore, the pixels corresponding to the vegetation areas and the pixels corresponding to the non-vegetation areas can be judged more accurately, and the change of the vegetation areas can be estimated more accurately.
The operation of determining vegetation change status information in the predetermined area according to the determined vegetation area and the preset reference area information, further comprises: determining a degradation area of green vegetation degradation according to the determined vegetation area and preset reference area information; and determining the reason for the degradation of the green vegetation in the degradation area according to the ground object type of the end member with the largest abundance value in the fifth pixel corresponding to the degradation area.
Specifically, after determining the third pixel 201a corresponding to the vegetation region and the fourth pixel 201b corresponding to the non-vegetation region, the computing device compares the third pixel 201a and the fourth pixel 201b with the reference region information, thereby determining the fifth pixel corresponding to the degradation region of vegetation degradation in the fourth pixel 201 b.
For example, in the case where the reference area information indicates that a certain ground area should be a vegetation area, but a pixel corresponding to the ground area is the fourth pixel 201b, it may be determined that the ground area is a degradation area where vegetation is degraded, and the corresponding pixel is the fifth pixel.
The computing device may then determine a cause of degradation of the green vegetation in the degradation region by analyzing a fifth type of the end member having the greatest abundance value in the fifth pixel. For example, when the end member with the greatest abundance value in the fifth pixel is a building end member, then it can be determined that the cause of the green vegetation degradation is that someone is using the vegetation area for building a building facility; when the end member with the largest abundance value in the fifth pixel is an agricultural land end member, the reason for the degradation of the green vegetation can be determined as that a person cultivates the ground area, thereby causing the degradation of the green vegetation. And so on.
Therefore, according to the embodiment of the disclosure, the reason for degradation of the vegetation region can be accurately analyzed based on the abundance value of the pixel, so that the change state of the vegetation region can be more accurately and comprehensively evaluated.
Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium includes a stored program, wherein the method of any one of the above is performed by a processor when the program is run.
Thus, in embodiments of the present disclosure, rather than identifying the edges of the vegetation region by image recognition, the edges of the vegetation region are determined by analyzing the abundance values of the green vegetation end members in each pixel and based on the abundance values. Therefore, compared with an image recognition mode, the method and the device can extract the edge of the vegetation image area based on the mixed pixels of the reflected remote sensing image more accurately, and further accurately determine the vegetation area in the preset area, so that the change of the vegetation area is estimated accurately. The method solves the technical problems that the fuzzy edge of the vegetation area can not be accurately identified in the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
Example 2
Fig. 6 shows an abundance value-based vegetation-status monitoring apparatus 600 according to the first aspect of the present embodiment, the apparatus 600 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 6, the apparatus 600 includes: a first pixel determining module 610, configured to determine a plurality of first pixels corresponding to a predetermined area on the ground according to a satellite remote sensing reflection image generated based on a ground reflection spectrum signal; a vegetation abundance value determining module 620, configured to determine, according to the spectral feature corresponding to the first pixel, a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel; a second pixel determining module 630 for determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value; a vegetation region determining module 640 for determining a vegetation region based on the second pixel; and a vegetation change status determination module 650 for determining vegetation change status information in a predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
Optionally, vegetation abundance value determination module 620 includes: the reference spectrum characteristic acquisition sub-module is used for acquiring reference spectrum characteristics corresponding to end members of different ground object types; and an abundance value determining submodule, configured to determine abundance values of end members of different ground object types in the first pixel based on the following formulas according to the reference spectral feature and the spectral feature corresponding to the first pixel:
k 0 +k 1 F 1 +k 2 F 2 +k 3 F 3 +k 4 F 4 +k 5 F 5 =fd, where
Fd is the spectral feature of the first pixel; f (F) 1 Is the reference frequency spectrum characteristic of the green vegetation end corresponding to the vegetation ground type; k (k) 1 The abundance value of the green vegetation end member in the first pixel; f (F) 2 Is a reference spectral feature of a building end member corresponding to a building type of the building; k (k) 2 An abundance value of the building end member in the first pixel; f (F) 3 Is the reference frequency spectrum characteristic of the air-ground end member corresponding to the air-ground object type; k (k) 3 Is the abundance value of the air-ground end member in the first pixel; f (F) 4 Is a reference spectral feature of an agricultural land end member corresponding to an agricultural land object type; 4 the abundance value of the agricultural land end member in the first pixel; f (F) 5 Is the reference frequency spectrum characteristic of the water body end member corresponding to the water body ground object type; k (k) 5 The abundance value of the water body end member in the first pixel; k 0 Is the error term to be determined.
Optionally, the second pixel determination module 630 includes: a first pixel determining submodule, configured to determine a third pixel corresponding to a vegetation area and a fourth pixel corresponding to a non-vegetation area in the first pixel according to the vegetation abundance value; and a second pixel determination sub-module for determining the second pixel according to the third pixel and the fourth pixel.
Optionally, the first pixel determination submodule includes: the first judging unit is used for judging that the first pixel is a third pixel under the condition that the vegetation abundance value of the first pixel is larger than the abundance value of other ground object types.
Optionally, the first pixel determination submodule further includes: the second judging unit is used for judging that the first pixel is a fourth pixel under the condition that the end member with the largest abundance value in the first pixel is a building end member, an agricultural land end member or a water body end member.
Optionally, the first pixel determination submodule further includes: a historical statistical information unit, configured to obtain historical statistical information related to a vegetation abundance value of the first pixel when the end member with the largest abundance value in the first pixel is an air-ground end member; and a third judging unit for judging the first pixel as a third pixel or a fourth pixel according to the history statistical information.
Optionally, the vegetation change status determination module 650 includes: the degradation area determining submodule is used for determining a degradation area of green vegetation degradation according to the determined vegetation area and preset reference area information; and the degradation cause determining submodule is used for determining the cause of degradation of the green vegetation in the degradation area according to the ground object type of the end member with the largest abundance value in the fifth pixel element corresponding to the degradation area.
Thus, in embodiments of the present disclosure, rather than identifying the edges of the vegetation region by image recognition, the edges of the vegetation region are determined by analyzing the abundance values of the green vegetation end members in each pixel and based on the abundance values. Therefore, compared with an image recognition mode, the method and the device can extract the edge of the vegetation image area based on the mixed pixels of the reflected remote sensing image more accurately, and further accurately determine the vegetation area in the preset area, so that the change of the vegetation area is estimated accurately. The method solves the technical problems that the fuzzy edge of the vegetation area can not be accurately identified in the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated.
Example 3
Fig. 7 shows an abundance value-based vegetation-status monitoring apparatus 700 according to the first aspect of the present embodiment, the apparatus 700 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 7, the apparatus 700 includes: a processor 710; and a memory 720 coupled to the processor 710 for providing instructions to the processor for processing the steps of: determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal; determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel; determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value; determining a vegetation region according to the second pixel; and determining vegetation change state information in a predetermined area according to the determined vegetation area and preset reference area information, wherein the reference area is an area predetermined for planting vegetation in the predetermined area.
Optionally, the operation of determining the vegetation abundance value of the end member corresponding to the vegetation ground type in the first pixel according to the spectral feature corresponding to the first pixel comprises: acquiring reference spectrum characteristics corresponding to end members of different ground object types; and determining abundance values of the end members of different ground object types in the first pixel based on the following formulas according to the reference spectral feature and the spectral feature corresponding to the first pixel:
k 0 +k 1 F 1 +k 2 F 2 +k 3 F 3 +k 4 F 4 +k 5 F 5 =fd, where
Fd is the spectral feature of the first pixel; f (F) 1 Is the reference frequency spectrum characteristic of the green vegetation end corresponding to the vegetation ground type; k (k) 1 The abundance value of the green vegetation end member in the first pixel; f (F) 2 Is a reference spectral feature of a building end member corresponding to a building type of the building; k (k) 2 An abundance value of the building end member in the first pixel; f (F) 3 Is the reference frequency spectrum characteristic of the air-ground end member corresponding to the air-ground object type; k (k) 3 Is the abundance value of the air-ground end member in the first pixel; f (F) 4 Is a reference spectral feature of an agricultural land end member corresponding to an agricultural land object type; k (k) 4 The abundance value of the agricultural land end member in the first pixel; f (F) 5 Is the reference frequency spectrum characteristic of the water body end member corresponding to the water body ground object type; k (k) 5 The abundance value of the water body end member in the first pixel; k 0 Is the error term to be determined.
Optionally, determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value, including: determining a third pixel corresponding to the vegetation region and a fourth pixel corresponding to the non-vegetation region in the first pixel according to the vegetation abundance value; and determining the second pixel according to the third pixel and the fourth pixel.
Optionally, determining, from the vegetation abundance values, the third pixel corresponding to the vegetation region and the fourth pixel corresponding to the non-vegetation region in the first pixel, including: and under the condition that the vegetation abundance value of the first pixel is larger than the abundance value of the end members of other ground object types, judging that the first pixel is a third pixel.
Optionally, the operation of determining the third pixel corresponding to the vegetation area and the fourth pixel corresponding to the non-vegetation area in the first pixel according to the vegetation abundance value further includes: and under the condition that the end member with the largest abundance value in the first pixel is a building end member, an agricultural land end member or a water body end member, judging that the first pixel is a fourth pixel.
Optionally, the operation of determining the third pixel corresponding to the vegetation area and the fourth pixel corresponding to the non-vegetation area in the first pixel according to the vegetation abundance value further includes: acquiring historical statistical information related to the vegetation abundance value of the first pixel under the condition that the end member with the largest abundance value in the first pixel is an air-ground end member; and determining the first pixel as a third pixel or a fourth pixel according to the historical statistical information.
Optionally, the operation of determining vegetation change status information in the predetermined area according to the determined vegetation area and the preset reference area information further includes: determining a degradation area of green vegetation degradation according to the determined vegetation area and preset reference area information; and determining the reason for the degradation of the green vegetation in the degradation area according to the ground object type of the end member with the largest abundance value in the fifth pixel corresponding to the degradation area.
Thus, in embodiments of the present disclosure, rather than identifying the edges of the vegetation region by image recognition, the edges of the vegetation region are determined by analyzing the abundance values of the green vegetation end members in each pixel and based on the abundance values. Therefore, compared with an image recognition mode, the method and the device can extract the edge of the vegetation image area based on the mixed pixels of the reflected remote sensing image more accurately, and further accurately determine the vegetation area in the preset area, so that the change of the vegetation area is estimated accurately. The method solves the technical problems that the fuzzy edge of the vegetation area can not be accurately identified in the image identification mode in the prior art, and further the change of the vegetation area can not be accurately estimated.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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 Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A vegetation state monitoring method based on abundance values, comprising:
determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal;
determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel;
determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value;
determining the vegetation region according to the second pixel; and
and determining vegetation change state information in the preset area according to the determined vegetation area and preset reference area information, wherein the reference area is an area preset in the preset area for planting vegetation.
2. The method of claim 1, wherein determining a vegetation abundance value of an end member corresponding to a vegetation ground type in the first pixel from a spectral feature corresponding to the first pixel comprises:
Acquiring reference spectrum characteristics corresponding to end members of different ground object types; and
according to the reference spectrum characteristic and the spectrum characteristic corresponding to the first pixel, determining abundance values of end members of different ground object types in the first pixel based on the following formula:
k 0 +k 1 F 1 +k 2 F 2 +k 3 F 3 +k 4 F 4 +k 5 F 5 =fd, where
Fd is the spectral feature of the first pixel;
F 1 is the reference frequency spectrum characteristic of the green vegetation end corresponding to the vegetation ground type;
k 1 the abundance value of the green vegetation end member in the first pixel;
F 2 is a reference spectral feature of a building end member corresponding to a building type of the building;
k 2 an abundance value of the building end member in the first pixel;
F 3 is the reference frequency spectrum characteristic of the air-ground end member corresponding to the air-ground object type;
k 3 is the abundance value of the air-ground end member in the first pixel;
F 4 is a reference spectral feature of an agricultural land end member corresponding to an agricultural land object type;
k 4 the abundance value of the agricultural land end member in the first pixel;
F 5 is the reference frequency spectrum characteristic of the water body end member corresponding to the water body ground object type;
k 5 for the end member of the water body in the first imageAbundance values in the cells; and
k 0 is the error term to be determined.
3. The method of claim 2, wherein determining a second pixel corresponding to an edge of a vegetation region in the predetermined region based on the determined vegetation abundance value comprises:
Determining a third pixel corresponding to a vegetation area and a fourth pixel corresponding to a non-vegetation area in the first pixel according to the vegetation abundance value; and
and determining the second pixel according to the third pixel and the fourth pixel.
4. The method of claim 3, wherein determining, from the vegetation abundance values, a third pixel corresponding to a vegetation area and a fourth pixel corresponding to a non-vegetation area in the first pixel comprises: and under the condition that the vegetation abundance value of the first pixel is larger than the abundance value of other ground object types, judging that the first pixel is a third pixel.
5. The method of claim 4, wherein determining, from the vegetation abundance values, a third pixel corresponding to a vegetation area and a fourth pixel corresponding to a non-vegetation area in the first pixel, further comprises:
and under the condition that the end member with the largest abundance value in the first pixel is a building end member, an agricultural land end member or a water body end member, judging that the first pixel is a fourth pixel.
6. The method of claim 5, wherein determining, from the vegetation abundance values, a third pixel corresponding to a vegetation area and a fourth pixel corresponding to a non-vegetation area in the first pixel, further comprises:
Acquiring historical statistical information related to the vegetation abundance value of the first pixel under the condition that the end member with the largest abundance value in the first pixel is an air-ground end member; and
and judging the first pixel to be a third pixel or a fourth pixel according to the historical statistical information.
7. The method of claim 6, wherein determining vegetation change status information in the predetermined area based on the determined vegetation area and predetermined reference area information, further comprising:
determining a degradation area of green vegetation degradation according to the determined vegetation area and preset reference area information; and
and determining the reason of the degradation of the green vegetation in the degradation area according to the ground object type of the end member with the largest abundance value in the fifth pixel corresponding to the degradation area.
8. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 7 is performed by a processor when the program is run.
9. An abundance-based vegetation state monitoring device, comprising:
the first pixel determining module is used for determining a plurality of first pixels corresponding to a preset area on the ground according to a satellite remote sensing reflected image generated based on the ground reflection spectrum signal;
The vegetation abundance value determining module is used for determining the vegetation abundance value of the end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel;
a second pixel determining module, configured to determine a second pixel corresponding to an edge of a vegetation region in the predetermined region according to the determined vegetation abundance value;
a vegetation region determining module, configured to determine the vegetation region according to the second pixel; and
the vegetation change state determining module is used for determining vegetation change state information in the preset area according to the determined vegetation area and preset reference area information, wherein the reference area is an area which is preset in the preset area and used for planting vegetation.
10. An abundance-based vegetation state monitoring device, comprising:
a processor; and
a memory, coupled to the processor, for providing instructions to the processor to process the following processing steps:
determining a plurality of first pixels corresponding to a predetermined area on the ground based on a satellite remote sensing reflected image generated based on the ground reflection spectrum signal;
determining a vegetation abundance value of an end member corresponding to the vegetation ground type in the first pixel according to the frequency spectrum characteristic corresponding to the first pixel;
Determining a second pixel corresponding to an edge of the vegetation region in the predetermined region according to the determined vegetation abundance value;
determining the vegetation region according to the second pixel; and
and determining vegetation change state information in the preset area according to the determined vegetation area and preset reference area information, wherein the reference area is an area preset in the preset area for planting vegetation.
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