A method for extracting grassland vegetation based on radar remote sensing images

Abstract

The present application relates to a grassland vegetation extraction method based on radar remote sensing images, comprising the following steps: presetting a scattering mechanism in a detection area, the detection area including a dry vegetation area and a wet vegetation area; the presetting scattering mechanism includes a plurality of candidate points; obtaining a radar remote sensing image of the detection area, and calculating the coherence coefficients of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image; obtaining the corresponding coherence coefficient amplitude value using the coherence coefficient corresponding to the candidate points in the dry vegetation area and the wet vegetation; establishing the probability density function of the dry vegetation area and the wet vegetation area using the magnitude of the coherence coefficient amplitude value and the distribution position of the corresponding candidate point; and generating a probability distribution map of the detection area; and obtaining the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map. By setting the candidate points and matching the reflectivity of the candidate points with the reflectivity of the dry and wet vegetation, the distribution of the wet vegetation and the dry vegetation can be quickly known.

Description

Technical Field

[0001] The present application relates to the field of remote sensing detection technology, and in particular to a grassland vegetation extraction method based on radar remote sensing images. Background Art

[0002] At present, while coal mining promotes regional economic and social development, it also has serious negative impacts on the local ecological environment, the most prominent of which is ground subsidence and its impact on ecological disturbance.

[0003] In some related technologies, shield machines are used for construction and mining in the process of coal mining. The construction management and integrated construction requirements of the whole process of shield construction, excavation and arrival are mainly based on BIM technology support. Break through the construction mode of strip division, use the Internet and the Internet of Things to further improve the speed of internal information sharing, effectively coordinate the relationship between the various components of the construction, strengthen the monitoring and management of the construction, refine and intensively manage the shield construction to the greatest extent, reduce the safety risks of construction, and obtain the area of ​​the construction base and the surrounding regional environment, so as to achieve reasonable planning and green construction without large-scale ecological damage.

[0004] In other related technologies, coal mining in grasslands, Loess Plateau and other areas has further deteriorated the originally fragile ecological environment. In order to promote green mining and curb the damage to the ecological environment in mining areas, it is urgently necessary to repair the ecology of mining areas. However, before the restoration, it is necessary to obtain the surface vegetation conditions in order to propose targeted governance. The existing technology is generally viewed through satellite technology, but the image information obtained is redundant and the data is huge, and it is impossible to effectively and quickly know the distribution of wet vegetation and dry vegetation. This is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiment of the present application provides a grassland vegetation extraction method based on radar remote sensing images to solve the problem that the image information obtained in the related technology is complicated, the data is huge, and the distribution of wet vegetation and dry vegetation cannot be known effectively and quickly.

[0006] In a first aspect, a grassland vegetation extraction method based on radar remote sensing images is provided, comprising the following steps:

[0007] A scattering mechanism is preset in a detection area, wherein the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points;

[0008] Acquire a radar remote sensing image of the detection area, and calculate the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0009] Obtaining corresponding coherence coefficient amplitude values ​​using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation;

[0010] The probability density functions of dry vegetation areas and wet vegetation areas are established by using the magnitude of the coherence coefficient amplitude and the distribution positions of the corresponding candidate points.

[0011] Generate a probability distribution map of the detection area according to the probability density function;

[0012] According to the probability distribution map, the actual spatial distribution range of the dry vegetation area and the wet vegetation area is obtained.

[0013] In some embodiments, obtaining a radar remote sensing image of a detection area and calculating the coherence coefficients corresponding to dry vegetation areas and wet vegetation areas in the radar remote sensing image include the following steps:

[0014] Carry out radiometric calibration and atmospheric correction for radar remote sensing images;

[0015] Obtaining the reflectivity of the candidate point and the overall reflectivity of the detection area;

[0016] The reflectivity of all candidate points and the overall reflectivity of the detection area are used to obtain the coherence coefficient of the reflectivity of the candidate points relative to the overall reflectivity.

[0017] In some embodiments, obtaining corresponding coherence coefficient amplitude values ​​using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation includes the following steps:

[0018] Find the coherence coefficient amplitude value corresponding to each candidate point in the coherence coefficient;

[0019] Select candidate points with the same coherence coefficient amplitude value and perform the following steps:

[0020] The area around the candidate point with the same coherence coefficient amplitude value is taken as the secondary candidate point, the radar remote sensing image of the detection area is repeatedly obtained, and the coherence coefficients of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image are calculated; the corresponding coherence coefficient amplitude value is obtained using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation.

[0021] In some embodiments, the method further includes obtaining corresponding reflectances of the dry vegetation area and the wet vegetation area, obtaining a coherence coefficient of the reflectances of the vegetation area and the wet vegetation area relative to the overall reflectance, and a coherence coefficient amplitude value of the dry vegetation area and the wet vegetation area;

[0022] The coherence coefficient amplitude values ​​of all candidate points are compared with the coherence coefficient amplitude values ​​of the dry vegetation area and the wet vegetation area respectively;

[0023] If the coherence coefficient amplitude value of the candidate point is within the error range of the coherence coefficient amplitude value of the dry vegetation area, then the candidate point is located in the dry vegetation area, and the position of the candidate point is recorded;

[0024] Otherwise the candidate point is located in the wet vegetation area.

[0025] In some embodiments, in the above steps, the formula used for radiation calibration is:

[0026] L=DN÷Gain+Bias

[0027] Among them, DN is the grayscale value, L is the radiation brightness of the object at the radar entrance pupil, Gain is the calibration gain parameter, and Bias is the offset parameter.

[0028] In some embodiments, after radiometric calibration, the flight time of the satellite radar, the longitude and latitude of the center of the scene, the radar reflection power, and the average ground elevation are input;

[0029] Then, atmospheric correction is performed based on the latitude and seasonal conditions of the detection area.

[0030] In some embodiments, after generating a probability distribution map of the detection area according to the probability density function, and before obtaining the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map, the following steps are also included:

[0031] Dividing the probability distribution map into regions to form multiple optimization regions;

[0032] Acquire a radar remote sensing image corresponding to the optimization area, and calculate the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0033] The corresponding coherence coefficient amplitude value is obtained by using the coherence coefficient corresponding to the candidate point in the optimized region;

[0034] According to the magnitude and distribution of the coherence coefficient amplitude value, the probability density function of the vegetation area and the wet vegetation area in the optimization area is established;

[0035] A probability distribution diagram of the optimization area is generated according to the probability density function.

[0036] In some embodiments, artificial objects or natural objects with strong echo signals and relatively stable scattering characteristics are selected as candidate points in the detection area.

[0037] In some embodiments, the man-made features include roofs, metal and concrete objects; and the natural features include rocks and water surfaces.

[0038] In a second aspect, a grassland vegetation extraction system based on radar remote sensing images is provided, which includes:

[0039] A first module is used to preset a scattering mechanism in a detection area, wherein the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points;

[0040] The second module is used to obtain the radar remote sensing image of the detection area and calculate the coherence coefficient of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0041] A third module is used to obtain corresponding coherence coefficient amplitude values ​​from the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation;

[0042] The fourth module is used to establish the probability density function of the dry vegetation area and the wet vegetation area by using the magnitude and distribution position of the coherence coefficient amplitude value;

[0043] A fifth module, which is used to generate a probability distribution map of the detection area according to the probability density function;

[0044] The sixth module is used to obtain the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map.

[0045] The beneficial effects of the technical solution provided by this application include:

[0046] An embodiment of the present application provides a grassland vegetation extraction method based on radar remote sensing images, which uses radar remote sensing to detect images, then obtains candidate points in the image, obtains the coherence coefficient through the reflectivity of the candidate points and the reflectivity of dry and wet vegetation, finds the coherence coefficient amplitude value in the coherence coefficient, and obtains the probability density function of the detection area through the size of the coherence coefficient amplitude value and the distribution position of the corresponding candidate point, thereby quickly obtaining the distribution of dry and wet vegetation in the detection area. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A schematic diagram of the general steps of the grassland vegetation extraction method based on radar remote sensing images provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0050] The embodiment of the present application provides a grassland vegetation extraction system and method based on radar remote sensing images to solve the problem that the image information obtained in the related technology is complicated, the data is huge, and the distribution of wet vegetation and dry vegetation cannot be known effectively and quickly.

[0051] See also Figure 1 A grassland vegetation extraction method based on radar remote sensing images, characterized in that it comprises the following steps:

[0052] S1. Preset a scattering mechanism in a detection area, where the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points;

[0053] S2, obtaining a radar remote sensing image of the detection area, and calculating the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0054] S3, using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation to obtain corresponding coherence coefficient amplitude values;

[0055] S4, using the magnitude of the coherence coefficient amplitude value and the distribution position of the corresponding candidate points, establishing the probability density function of the dry vegetation area and the wet vegetation area;

[0056] S5. Generate a probability distribution map of the detection area according to the probability density function;

[0057] S6. According to the probability distribution map, the actual spatial distribution range of the dry vegetation area and the wet vegetation area is obtained.

[0058] Based on satellite remote sensing, SPOT2 / 4 satellite data is mainly used for data fusion and extraction of vegetation indices such as NDVI, among which the multispectral band resolution is 20m and the panchromatic fluctuation band resolution is 10m. SPOT single-band images can judge certain terrain features on grayscale images. SPOT color composite images have rich image color information and have the advantage of a spatial resolution of 10m. Therefore, the remote sensing images obtained meet the operational requirements in the above steps.

[0059] Through the above steps, by setting candidate points and obtaining the reflectivity of the candidate points and the reflectivity of dry and wet vegetation, the coherence coefficient is obtained, the coherence coefficient amplitude value is found in the coherence coefficient, and the probability density function of the detection area is obtained by the size of the coherence coefficient amplitude value and the distribution position of the corresponding candidate point, so as to quickly obtain the distribution of dry and wet vegetation in the detection area, and the distribution of wet vegetation and dry vegetation in the detection area can be quickly known without other complex calculations.

[0060] In some preferred embodiments, obtaining a radar remote sensing image of a detection area and calculating the coherence coefficients corresponding to dry vegetation areas and wet vegetation areas in the radar remote sensing image include the following steps:

[0061] Radiation calibration and atmospheric correction are performed on radar remote sensing images; the formula used for radiation calibration is:

[0062] L=DN÷Gain+Bias

[0063] Among them, DN is the grayscale value, L is the radiation brightness of the object at the radar entrance pupil, Gain is the calibration gain parameter, and Bias is the offset parameter.

[0064] Among them, after the radiation calibration, the flight time of the satellite radar, the longitude and latitude of the center of the scene, the radar reflection power, and the average ground elevation are input;

[0065] Then, atmospheric correction is performed based on the latitude and seasonal conditions of the detection area to accurately obtain the candidate reflectivity.

[0066] Obtain the reflectivity of the candidate points and the overall reflectivity of the detection area;

[0067] The reflectivity of all candidate points and the overall reflectivity of the detection area are used to obtain the coherence coefficient of the reflectivity of the candidate points relative to the overall reflectivity.

[0068] In some preferred embodiments, the applicable condition is that all candidate points can represent the reflectivity of their locations, but the reflectivity of some candidate points is greatly different from the reflectivity of the surroundings of their actual locations, which results in candidate points that should be wet vegetation areas being treated as candidate points of dry vegetation areas. Therefore, the following settings are made:

[0069] The method also includes obtaining the corresponding reflectances of the dry vegetation area and the wet vegetation area, obtaining the coherence coefficients of the reflectances of the vegetation area and the wet vegetation area relative to the overall reflectance, and the amplitude values ​​of the coherence coefficients of the dry vegetation area and the wet vegetation area;

[0070] The coherence coefficient amplitude values ​​of all candidate points are compared with the coherence coefficient amplitude values ​​of the dry vegetation area and the wet vegetation area respectively;

[0071] If the coherence coefficient amplitude value of the candidate point is within the error range of the coherence coefficient amplitude value of the dry vegetation area, then the candidate point is located in the dry vegetation area, and the position of the candidate point is recorded;

[0072] Otherwise the candidate point is located in the wet vegetation area.

[0073] In some preferred embodiments, there is a situation where the above steps are not enough to distinguish, that is, there is a water source at the candidate point of the dry vegetation area, and there is a small piece of green plants around the water source, but it should be considered as a dry vegetation area. To deal with this situation, the following steps are taken:

[0074] Find the coherence coefficient amplitude value corresponding to each candidate point in the coherence coefficient;

[0075] Select candidate points with the same coherence coefficient amplitude value and perform the following steps:

[0076] The area around the candidate point with the same coherence coefficient amplitude value is taken as the secondary candidate point, the radar remote sensing image of the detection area is repeatedly obtained, and the coherence coefficients of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image are calculated; the corresponding coherence coefficient amplitude value is obtained using the coherence coefficient corresponding to the candidate point in the dry vegetation area and the wet vegetation.

[0077] In some preferred embodiments, after generating a probability distribution map of the detection area according to the probability density function, and before obtaining the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map, the following steps are also included:

[0078] Divide the probability distribution map into regions to form multiple optimization regions;

[0079] Acquire a radar remote sensing image corresponding to the optimization area, and calculate the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0080] The corresponding coherence coefficient amplitude value is obtained by using the coherence coefficient corresponding to the candidate point in the optimized region;

[0081] According to the magnitude and distribution of the coherence coefficient amplitude value, the probability density function of the vegetation area and the wet vegetation area in the optimization area is established;

[0082] According to the probability density function, a probability distribution map of the optimization area is generated.

[0083] The above steps refine the probability distribution map, and the steps of obtaining the probability distribution map are repeated, so that its accuracy can be increased and a large probability distribution map can be divided into multiple small probability distribution maps.

[0084] In some preferred embodiments, artificial objects or natural objects with strong echo signals and relatively stable scattering characteristics are selected as candidate points in the detection area. Artificial objects include roofs, metal and concrete objects; natural objects include rocks and water surfaces.

[0085] This application also proposes a grassland vegetation extraction system based on radar remote sensing images, which includes:

[0086] The first module is used to preset a scattering mechanism in a detection area, the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points;

[0087] The second module is used to obtain the radar remote sensing image of the detection area and calculate the coherence coefficient of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image;

[0088] The third module is used to obtain the corresponding coherence coefficient amplitude value of the coherence coefficient corresponding to the candidate points in the dry vegetation area and the wet vegetation;

[0089] The fourth module is used to establish the probability density function of the dry vegetation area and the wet vegetation area by using the magnitude and distribution position of the coherence coefficient amplitude value;

[0090] A fifth module is used to generate a probability distribution map of the detection area according to the probability density function;

[0091] The sixth module is used to obtain the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map.

[0092] This system can match the above-mentioned grassland vegetation extraction method based on radar remote sensing images, and find the coherence coefficient amplitude value in the coherence coefficient. The probability density function of the detection area is obtained by the size of the coherence coefficient amplitude value and the distribution position of the corresponding candidate point, so as to quickly obtain the distribution of dry and wet vegetation in the detection area, which provides important value for the reliable evaluation of the effective area of ​​grassland and the analysis of its degradation degree.

[0093] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0094] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0095] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A grassland vegetation extraction method based on radar remote sensing images, characterized in that: The following steps are involved: A scattering mechanism is preset in a detection area, wherein the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points; Acquire a radar remote sensing image of the detection area, and calculate the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image; Obtaining corresponding coherence coefficient amplitude values ​​using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation; The probability density functions of dry vegetation areas and wet vegetation areas are established by using the magnitude of the coherence coefficient amplitude and the distribution positions of the corresponding candidate points. Generate a probability distribution map of the detection area according to the probability density function; According to the probability distribution map, the actual spatial distribution range of the dry vegetation area and the wet vegetation area is obtained; Acquiring a radar remote sensing image of the detection area and calculating the coherence coefficients corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image include the following steps: Carry out radiometric calibration and atmospheric correction for radar remote sensing images; Obtaining the reflectivity of the candidate point and the overall reflectivity of the detection area; Using the reflectivity of all candidate points and the overall reflectivity of the detection area, the coherence coefficient of the reflectivity of the candidate points relative to the overall reflectivity is obtained; Obtaining corresponding coherence coefficient amplitude values ​​using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation includes the following steps: Find the coherence coefficient amplitude value corresponding to each candidate point in the coherence coefficient; Select candidate points with the same coherence coefficient amplitude value and perform the following steps: The area around the candidate point with the same coherence coefficient amplitude value is taken as the secondary candidate point, the radar remote sensing image of the detection area is repeatedly obtained, and the coherence coefficients of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image are calculated; the corresponding coherence coefficient amplitude value is obtained using the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation.

2. The grassland vegetation extraction method based on radar remote sensing images as claimed in claim 1, characterized in that: The method also includes obtaining the corresponding reflectances of the dry vegetation area and the wet vegetation area, obtaining the coherence coefficients of the reflectances of the vegetation area and the wet vegetation area relative to the overall reflectance, and the amplitude values ​​of the coherence coefficients of the dry vegetation area and the wet vegetation area; The coherence coefficient amplitude values ​​of all candidate points are compared with the coherence coefficient amplitude values ​​of the dry vegetation area and the wet vegetation area respectively; If the coherence coefficient amplitude value of the candidate point is within the error range of the coherence coefficient amplitude value of the dry vegetation area, the candidate point is located in the dry vegetation area, and the position of the point is recorded; Otherwise the candidate point is located in the wet vegetation area.

3. The grassland vegetation extraction method based on radar remote sensing images as claimed in claim 2, characterized in that: In the above steps, the formula used for radiation calibration is: L=DN÷Gain+Bias Among them, DN is the grayscale value, L is the radiation brightness of the object at the radar entrance pupil, Gain is the calibration gain parameter, and Bias is the offset parameter.

4. The grassland vegetation extraction method based on radar remote sensing images as claimed in claim 3, characterized in that: After radiometric calibration, input the flight time of the satellite radar, longitude and latitude of the center of the scene, radar reflection power, and average ground elevation; Then, atmospheric correction is performed based on the latitude and seasonal conditions of the detection area.

5. The grassland vegetation extraction method based on radar remote sensing images according to claim 1, characterized in that: After generating a probability distribution map of the detection area according to the probability density function, and before obtaining the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map, the following steps are also included: Dividing the probability distribution map into regions to form multiple optimization regions; Acquire a radar remote sensing image corresponding to the optimization area, and calculate the coherence coefficients of candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image; The corresponding coherence coefficient amplitude value is obtained by using the coherence coefficient corresponding to the candidate point in the optimized region; According to the magnitude and distribution of the coherence coefficient amplitude value, the probability density function of the vegetation area and the wet vegetation area in the optimization area is established; A probability distribution diagram of the optimization area is generated according to the probability density function.

6. The grassland vegetation extraction method based on radar remote sensing images as claimed in claim 1, characterized in that: In the detection area, artificial or natural objects with strong echo signals and relatively stable scattering characteristics are selected as candidate points.

7. The grassland vegetation extraction method based on radar remote sensing images as claimed in claim 6, characterized in that: The man-made features include roofs, metal and concrete objects; the natural features include rocks and water surfaces.

8. A grassland vegetation extraction system based on radar remote sensing images, characterized in that: The method according to any one of claims 1 to 7 is adopted, which also includes: A first module is used to preset a scattering mechanism in a detection area, wherein the detection area includes a dry vegetation area and a wet vegetation area; the preset scattering mechanism includes a plurality of candidate points; The second module is used to obtain the radar remote sensing image of the detection area and calculate the coherence coefficient of the candidate points corresponding to the dry vegetation area and the wet vegetation area in the radar remote sensing image; A third module is used to obtain corresponding coherence coefficient amplitude values ​​from the coherence coefficients corresponding to the candidate points in the dry vegetation area and the wet vegetation; The fourth module is used to establish the probability density function of the dry vegetation area and the wet vegetation area by using the magnitude and distribution position of the coherence coefficient amplitude value; A fifth module, which is used to generate a probability distribution map of the detection area according to the probability density function; The sixth module is used to obtain the actual spatial distribution range of the dry vegetation area and the wet vegetation area according to the probability distribution map.

Images