A method and device for detecting growth after disaster of rice

Abstract

The application provides a rice disaster aftermath growth detection method and device, and belongs to the technical field of agricultural remote sensing, and comprises the following steps: determining a research area affected by disaster and acquiring SAR image data of the research area; after terrain correction, radiation calibration and filtering are performed on the SAR image data, normalized backscattering cross-section intensity is obtained; the backscattering cross-section intensity of VH polarization and the backscattering cross-section intensity of VV polarization are brought into a formula for calculation to obtain a normalized difference value index; the normalized difference value index is brought into a formula NDVI=a*NDBI+b for calculation to obtain a normalized vegetation index and to obtain the growth of rice through inversion. The technical scheme is based on SAR image to detect the growth of rice crops, utilizes the characteristics that SAR image imaging is not affected by weather factors and timely, detects the disaster situation of rice, solves the problem that optical image is not suitable for detection after a flood disaster, and guarantees the availability, timeliness and accuracy of data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of agricultural remote sensing, and particularly relates to a post-disaster growth detection method and device for rice. BACKGROUND

[0002] In recent years, extreme weather has become increasingly frequent. In the first three quarters of 2021, there were 39 strong rainfall processes in China, and the national surface precipitation was 582 mm, which was 4% higher than the same period of the previous year. Among them, from July to August 2021, Henan, Sichuan, Shanxi, Hebei and other places in China were severely affected by heavy rain and flood, and were severely damaged. In terms of crop production, the flood disaster caused by continuous rainfall submerged farmland, and then affected the growth of crops. The continuous rainy weather makes it difficult to obtain optical remote sensing images, especially in the cloudy and rainy conditions in the south, which prolongs the effective acquisition time of optical images. For post-disaster crop monitoring, optical images are difficult to guarantee the timeliness of data acquisition. In the condition of post-disaster crop monitoring, optical data is difficult to play an advantage. The use of synthetic aperture radar (SAR) images can overcome the dependence of optical data on weather conditions. SAR images use microwave bands for active imaging, and the microwave band is longer, can penetrate clouds and fog, and the imaging process is not affected by light. All-weather imaging can ensure effective monitoring of crops and timely acquisition of image of the monitoring area, which is of great significance for post-disaster crop monitoring.

[0003] The existing technology generally uses optical images for crop yield estimation, and there is a problem that the acquisition of optical image data cannot be guaranteed, especially in the continuous rainy conditions, the crop growth evaluation is affected by the data problem and cannot be carried out in time, and in the aspects of disaster insurance claims and post-disaster production self-help, timely crop disaster evaluation is particularly important. At present, SAR is generally used for crop monitoring based on single polarization or cross-polarization data and full-polarization data. Among them, the imaging range of full-polarization data is generally small, and the image acquisition cost is high. The widely used single polarization or cross-polarization data is mainly based on backscattering intensity, and the backscattering intensity is sensitive to the water content of the ground object. After the rainfall process, the scattering intensity fluctuates greatly, which affects the further analysis of the backscattering intensity on crops. SUMMARY

[0004] The application provides a post-disaster growth detection method and device for rice, which aims to solve the problems of time delay in optical image acquisition after flood disaster and large fluctuation of backscattering intensity after rainfall, which is not conducive to subsequent analysis of crops.

[0005] In order to achieve the above purpose, the following technical solutions are adopted in the application:

[0006] Determine a study area of a disaster and obtain SAR image data of the study area, after terrain correction, radiation calibration and filtering of the SAR image data, obtain normalized backscattering cross section intensity, the normalized backscattering cross section intensity includes VH polarized backscattering cross section intensity and VV polarized backscattering cross section intensity;

[0007] The VH polarized backscattering cross section intensity and the VV polarized backscattering cross section intensity are brought into the formula to calculate, and the normalized difference index is obtained, wherein NDBI is the normalized difference index, σ VH is the VH polarized backscattering cross section intensity, σ VV is the VV polarized backscattering cross section intensity.

[0008] The normalized difference index is brought into the formula NDVI=a*NDBI+b to calculate, and the normalized vegetation index is obtained, and the growth of rice is obtained by inversion, wherein NDVI is the normalized vegetation index, a is a coefficient, and b is a constant.

[0009] Preferably, the determination of the study area of the disaster comprises:

[0010] Obtain SAR images of a disaster area and contemporaneous SAR images of the disaster area, and respectively perform water body area extraction to obtain a first water body range and a second water body range;

[0011] Remove the intersection of the first water body range and the second water body range to obtain a third water body range;

[0012] Obtain a rice distribution map of the disaster area, and obtain the intersection of the rice distribution map and the third water body range to obtain the study area of the disaster.

[0013] Preferably, the calculation of the normalized vegetation index by bringing the normalized difference index into the formula NDVI=a*NDBI+b and the inversion of the growth of rice comprise:

[0014] If the detection period is before the heading of rice, the normalized difference index is brought into the formula NDVI=-2.9789*NDBI+1.2659 to calculate, and the first normalized vegetation index is obtained, and the first growth of rice is obtained by inversion, wherein -2.9789 is a coefficient a, and 1.2659 is a constant b;

[0015] If the detection period is after the heading of rice, the normalized difference index is brought into the formula NDVI=-5.2917*NDBI+1.4250 to calculate, and the second normalized vegetation index is obtained, and the second growth of rice is obtained by inversion, wherein -5.2917 is a coefficient a, and 1.4250 is a constant b.

[0016] As preferred, the inversion of the growth of rice after the situation also includes:

[0017] According to the historical NDVI of rice growth, the same normalized difference vegetation index (NDVI) is obtained in the disaster period, and a third normalized difference vegetation index (NDVI) is obtained. The third normalized difference vegetation index (NDVI) is compared with the first normalized difference vegetation index (NDVI) or the second normalized difference vegetation index (NDVI), and the disaster grade is divided according to a fixed threshold, and a disaster damage classification map is established.

[0018] A device for detecting the growth of rice after disaster, comprising:

[0019] An image data preprocessing module: for determining a study area affected by disaster and obtaining SAR image data of the study area, and after terrain correction, radiation calibration and filtering of the SAR image data, obtaining normalized backscatter cross-section intensity, the normalized backscatter cross-section intensity including VH polarized backscatter cross-section intensity and VV polarized backscatter cross-section intensity;

[0020] A normalized difference value index calculation module: for bringing the VH polarized backscatter cross-section intensity and the VV polarized backscatter cross-section intensity into the formula to calculate the normalized difference value index, wherein NDBI is the normalized difference value index, σ VH is the VH polarized backscatter cross-section intensity, and σ VV is the VV polarized backscatter cross-section intensity;

[0021] A rice growth inversion module: for bringing the normalized difference value index into the formula NDVI=a*NDBI+b to calculate the normalized difference vegetation index (NDVI) and invert the growth of rice, wherein NDVI is the normalized difference vegetation index, a is a coefficient, and b is a constant.

[0022] As preferred, the image data preprocessing module comprises:

[0023] A first study area calculation module: for obtaining SAR images of a disaster area and contemporaneous SAR images of the disaster area, and respectively performing water area extraction to obtain a first water area range and a second water area range;

[0024] A second study area calculation module: for removing the intersection of the first water area range and the second water area range to obtain a third water area range;

[0025] A third study area calculation module: for obtaining a rice distribution map of the disaster area, and obtaining the intersection of the rice distribution map and the third water area range to obtain the study area affected by disaster.

[0026] As preferred, the rice growth status inversion module comprises:

[0027] The rice growth status first calculation module is used for detecting a period before rice heading, and bringing the normalized difference index into a formula NDVI=-2.9789*NDBI+1.2659 to calculate a first normalized vegetation index and obtain a first growth status of the rice, wherein -2.9789 is a coefficient a and 1.2659 is a constant b.

[0028] The rice growth status second calculation module is used for detecting a period after rice heading, and bringing the normalized difference index into a formula NDVI=-5.2917*NDBI+1.4250 to calculate a second normalized vegetation index and obtain a second growth status of the rice, wherein -5.2917 is a coefficient a and 1.4250 is a constant b.

[0029] As preferred, the rice growth status inversion module further comprises:

[0030] The disaster damage grading map establishment module is used for obtaining a third normalized vegetation index according to the normalized vegetation index of historical rice growth in the same disaster period, comparing the third normalized vegetation index with the first normalized vegetation index or the second normalized vegetation index, and dividing disaster grades according to a fixed threshold to establish a disaster damage grading map.

[0031] A rice post-disaster growth status detection device comprises a memory and a processor, and the memory is used for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the rice post-disaster growth status detection method in any one of the above.

[0032] A computer readable storage medium storing a computer program, wherein the computer program is executed by a computer to implement the rice post-disaster growth status detection method in any one of the above.

[0033] The present application has the following advantages:

[0034] (1) The technical solution is based on SAR image to detect the growth status of rice crops, and utilizes the characteristics of SAR image imaging not affected by weather factors and timeliness to detect rice disaster, solves the problem that optical image is not suitable for post-flood disaster detection, and guarantees the data availability, timeliness and accuracy;

[0035] (2) The technical scheme adopts a normalized difference method to construct a normalized backscattering difference index of the radar, reduces the influence of the water body on the VV polarization and VH polarization data, facilitates subsequent further analysis, simultaneously adopts a harmonic filtering method, establishes a fitting function for the optical NDVI index of the rice and the corresponding index of the SAR image respectively, constructs the relationship between the NDVI and the NDBI, and then realizes quantitative characterization of the growth of the crops by the NDBI, so that the NDVI of the rice after the flood disaster can be accurately calculated, and the growth of the rice is inversed based on the NDVI, so that the detection of the growth of the rice after the disaster is more accurate;

[0036] (3) The technical scheme extracts the flooded area from the post-disaster SAR image, and combines the pre-disaster optical image, so that the affected area of the rice by the flood disaster can be extracted, the area affected by the flood disaster is obtained in time and accurately, and after the growth of the rice in the affected area is calculated, the historical growth at the same period is compared, a disaster damage classification map is established, the disaster area is evaluated, and the loss after the disaster is determined and subsequent disaster resistance and self-rescue are facilitated;

[0037] (4) The technical scheme constructs the relationship between the NDVI and the NDBI, and is divided into two periods before and after the rice heading, fully considers the influence of the growth characteristics of the rice on the NDVI, and then the NDVI obtained by the NDBI brought into the formula calculation is more accurate, and the accuracy of the growth detection of the rice by the scheme is improved. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A flowchart of a post-disaster growth detection method of rice is realized for the embodiment of the present application

[0039] Figure 2 A schematic diagram of a SAR image when a flood disaster occurs is for the embodiment of the present application

[0040] Figure 3 A schematic diagram of a historical SAR image at the same period is for the embodiment of the present application

[0041] Figure 4 A schematic diagram of flood water extraction is for the embodiment of the present application

[0042] Figure 5 A schematic diagram of a flood inundation range is for the embodiment of the present application

[0043] Figure 6 A schematic diagram of an image relationship before rice heading is for the embodiment of the present application

[0044] Figure 7 A schematic diagram of an image relationship after rice heading is for the embodiment of the present application

[0045] Figure 8A schematic diagram of a disaster damage grading in an embodiment of the present application

[0046] Figure 9 A schematic diagram of a typical crop radar backscattering intensity time series analysis in an embodiment of the present application

[0047] Figure 10 A schematic diagram of a typical crop normalized difference index and fitting characteristics in an embodiment of the present application

[0048] Figure 11 A schematic diagram of a typical crop NDVI time series analysis in an embodiment of the present application

[0049] Figure 12 A structural schematic diagram of a post-disaster growth detection device for rice in an embodiment of the present application

[0050] Figure 13 A structural schematic diagram of an image data preprocessing module 10 in a post-disaster growth detection device for rice in an embodiment of the present application

[0051] Figure 14 A structural schematic diagram of a rice growth inversion module 30 in a post-disaster growth detection device for rice in an embodiment of the present application

[0052] Figure 15 A schematic diagram of an electronic device in a post-disaster growth detection device for rice in an embodiment of the present application DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0054] The terms "first", "second", and the like in the claims and specification of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence, and it should be understood that the terms thus used can be interchanged, which is only a distinguishing way adopted in the description of the embodiments of the present application for the objects with the same attribute in the description, and in addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, so that the processes, methods, systems, products or devices containing a series of units do not have to be limited to those units, but can include other units not clearly listed or inherent to the processes, methods, products or devices.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application.

[0056] Embodiment 1

[0057] As Figure 1 shown, a rice post-disaster growth detection method comprises the following steps:

[0058] S11, determining a disaster-affected research area and obtaining SAR image data of the research area, performing terrain correction, radiation calibration and filtering on the SAR image data to obtain normalized backscatter cross-section intensity, the normalized backscatter cross-section intensity including VH-polarized backscatter cross-section intensity and VV-polarized backscatter cross-section intensity;

[0059] S12, bringing the VH-polarized backscatter cross-section intensity and the VV-polarized backscatter cross-section intensity into the formula to calculate a normalized difference value index, wherein NDBI is the normalized difference value index, σ VH is the VH-polarized backscatter cross-section intensity, and σ VV is the VV-polarized backscatter cross-section intensity.

[0060] S13, bringing the normalized difference value index into the formula NDVI=a*NDBI+b to calculate a normalized vegetation index and inversely obtain the growth of rice, wherein NDVI is the normalized vegetation index, a is a coefficient, and b is a constant.

[0061] In this embodiment, Poyang County is selected as a disaster-affected area for research and analysis. Since radar reflects ground information by receiving backscattered microwave information, different ground objects have different backscattering intensities in radar images due to factors such as shape, surface roughness, and dielectric constant. The surface of water is generally relatively smooth, and microwave information generally undergoes specular reflection on the surface of water, and the backscattering signal is relatively weak. That is, water in the radar image mainly appears in dark tones. Therefore, the flood-affected area in the radar image is darker than the non-flooded farmland area. SAR images of a certain range of land plots in Poyang County are obtained, as shown in Figures 2-5 , wherein Figure 2 is the SAR image of the area after the flood disaster, i.e., the SAR image of the disaster-affected area, Figure 3 is the SAR image of the area at the same period in history, i.e., the SAR image of the disaster-affected area at the same period, and Figure 3The darkest area in the color is the water body, so the "second water body range" after water body extraction from the SAR image of the same period in history is the darkest area in the color. Then the water body in the Figure 2 is extracted, and the water body range after extraction is the darkest area in the color in the Figure 4 , which is the "first water body range". Then the same area in the first water body range as the second water body range is removed, because these areas are the original water body areas in the plot and are not the areas caused by floods. After removal, the third water body range is obtained, which is shown in Figure 5 , and is a slightly lighter area compared with the darkest area. This area is the submerged area caused by floods in the local plot. Then because the Sentinel-2 optical data has a revisit period of 5 days and is widely used in global monitoring, sufficient data resources can be obtained before the occurrence of flood disasters, and the ground of the disaster area can be imaged. Therefore, according to the pre-disaster optical image, combined with the sample data of the ground in the disaster area, the classification of different crops can be realized, and the main crop distribution map of the monitoring area can be generated, so as to determine the rice distribution, and the rice distribution map of the local plot can be obtained. The rice distribution map and the third water body range are intersected to determine the common area, which is the rice disaster area caused by floods, that is, the "disaster research area" in the present technical solution;

[0062] After determining the disaster research area, SAR image data of the area is obtained. After terrain correction and radiation calibration of the obtained SAR image data, the normalized backscatter cross-section intensity, i.e. Normalized Radar Cross Section: NRCS, is obtained, which is defined as σ0=10log 10 σ[dB]. Because VV and VH are two polarization modes of data, the generated NRCS respectively contains the backscatter cross-section intensity of VV and the backscatter cross-section intensity of VH;

[0063] Then the normalized difference index calculation formula is used to bring the backscatter cross-section intensity of VV and the backscatter cross-section intensity of VH into calculation to obtain the NDBI index. In the formula, NDBI is the normalized difference index, σ VH is the backscatter cross-section intensity of VH polarization, and σ VV is the backscatter cross-section intensity of VV polarization;

[0064] Through the analysis of the growth of rice, it is found that there is a certain relationship between the growth of rice and SAR image, that is, the NDVI which can reflect the growth of rice and the NDBI obtained by processing SAR image data have a negative correlation. Therefore, the linear relationship of the relationship formula NDVI=a*NDBI+b is established for period-by-period fitting. In the formula, NDVI is the normalized vegetation index, a is the coefficient, and b is the constant. The relationship between the two in the research area is as shown inFigures 6-7 As shown, the two are the image relationship before heading and the image relationship after heading respectively, and then the relationship of different periods is constructed, which are NDVI = -2.9789 * NDBI + 1.2659 (before heading), wherein -2.9789 is the coefficient a, and 1.2659 is the constant b, NDVI = -5.2917 * NDBI + 1.4250 (after heading), wherein -5.2917 is the coefficient a, and 1.4250 is the constant b, after obtaining the above relationship, the corresponding NDBI value can be obtained through processing of the SAR image of the region, and then according to different periods, the above relationship is brought in to obtain the corresponding NDVI index representing the growth characteristics of the rice in the region, the calculated NDBI is brought into different relationship respectively according to different periods to obtain the first normalized vegetation index (first NDVI) and the second normalized vegetation index (second NDVI), and then according to the normalized vegetation index, the first growth and the second growth of the rice are obtained through inversion respectively;

[0065] After obtaining the NDVI of the disaster-affected rice in the research region, the NDVI of the same period as the disaster-affected period in the history is obtained through existing information, that is, the third normalized vegetation index, then the third normalized vegetation index is compared with the first normalized vegetation index and the second normalized vegetation index respectively, so as to know the disaster-affected situation of the rice, and finally the disaster damage classification map of the rice is established according to different disaster-affected situations, and the different level thresholds of the disaster damage classification map need to be determined in combination with the actual growth of the local crops, and generally the comparison with the multi-year NDVI average of the local rice can be adopted, and the anomaly model is defined as follows: Wherein is the multi-year NDVI average of the similar date of disaster occurrence, and NDVI is the calculated value at the time of disaster occurrence, through the actual change range, and then the classification processing is performed, and the framework of the disaster damage classification map is as shown in Figure 8

[0066] Through the above process, the growth detection of the disaster-affected rice in the research region and the disaster-affected situation analysis are completed.

[0067] The beneficial effects of the embodiment are as follows:

[0068] (1) The technical solution detects the growth of the rice crop based on the SAR image, utilizes the characteristics that the SAR image imaging is not affected by weather factors and timely, detects the disaster situation of the rice, solves the problem that the optical image is not suitable for detection after the flood disaster, and guarantees the availability, timeliness and accuracy of the data;

[0069] ​(2) The technical solution adopts a normalized difference method to construct a normalized backscatter difference index of the radar, reduces the influence of the water body on the VV polarization and VH polarization data, facilitates subsequent further analysis, and also makes the analysis result more accurate.

[0070] (3) The technical solution extracts a flooded area from the post-disaster SAR image, combines the pre-disaster optical image, realizes extraction of a disaster-affected area of the rice, obtains the area affected by the flood disaster in a timely and accurate manner, and after calculating the growth of the rice in the disaster-affected area, compares the growth with a historical growth at the same period, establishes a disaster damage classification map, and evaluates the disaster situation of the disaster-affected area, thereby facilitating loss determination after the disaster and subsequent disaster resistance and self-rescue.

[0071] (4) The technical solution constructs the relationship between the NDVI and the NDBI in two periods of before and after the rice heading, fully considers the influence of the growth characteristics of the rice on the NDVI image, and thus makes the NDVI obtained by bringing the NDBI into the formula calculation more accurate, and improves the accuracy of the rice growth detection of the technical solution.

[0072] Embodiment 2

[0073] A method for establishing a relationship between NDVI and NDBI, comprising:

[0074] Taking rice in Jiangsu as an example, the typical crop phenological characteristics of the example area are as follows: the rice in Jiangsu is mainly medium rice, is sowed and sprouted in May, is sprouted in June, is in the booting and heading period in the middle and late October, and is matured and harvested from the late October to the early November.

[0075] According to analysis, from June 2020 to July 2021, the SAR image VV and VH backscatter characteristics are as shown in Figure 9 , then the normalized difference index is calculated, and time series fitting is performed to obtain the time series fitting characteristics of the typical crops as shown in Figure 10 , at the same time, the corresponding area cloudless data are screened in combination with the optical image, the NDVI time series and the fitting result are as shown in Figure 11 , through comparison and analysis of Figure 10 and Figure 11 , it is found that there is a negative correlation between the NDVI and the NDBI, so a linear relationship of NDVI=a*NDBI+b can be constructed, the value of the NDVI is calculated through calculation of the NDBI, and the post-disaster growth of the rice is inversely calculated, so that the result is more accurate.

[0076] The beneficial effects of the embodiment are as follows:

[0077] The technical scheme adopts a simple harmonic filtering mode, fitting functions are respectively established for optical NDVI indexes of rice and corresponding indexes of SAR images, a relationship between NDVI and NDBI is constructed, and then the growth of crops is quantitatively characterized by NDBI, so that the NDVI of rice after being affected by a flood disaster can be accurately calculated, and the growth of rice is inversed based on the NDVI, so that the detection of the growth of rice after a disaster is more accurate.

[0078] Embodiment 3

[0079] As Figure 12 shown, a post-disaster growth detection device for rice includes:

[0080] An image data preprocessing module 10 is configured to determine a disaster-affected research area and acquire SAR image data of the research area, perform terrain correction, radiation calibration and filtering on the SAR image data, and obtain normalized backscattering cross-section intensity, wherein the normalized backscattering cross-section intensity includes VH-polarized backscattering cross-section intensity and VV-polarized backscattering cross-section intensity.

[0081] A normalized difference index calculation module 20 is configured to calculate the normalized difference index by inputting the VH-polarized backscattering cross-section intensity and the VV-polarized backscattering cross-section intensity into a formula , wherein NDBI is the normalized difference index, σ VH is the VH-polarized backscattering cross-section intensity, and σ VV is the VV-polarized backscattering cross-section intensity.

[0082] A rice growth inversion module 30 is configured to calculate the normalized vegetation index and obtain the growth of rice by inputting the normalized difference index into a formula NDVI=a*NDBI+b, wherein NDVI is the normalized vegetation index, a is a coefficient, and b is a constant.

[0083] In an embodiment of the device, in the image data preprocessing module 10, the disaster-affected research area is determined and the SAR image data of the research area is acquired, the SAR image data is subjected to terrain correction, radiation calibration and filtering, and the normalized backscattering cross-section intensity is obtained, wherein the normalized backscattering cross-section intensity includes the VH-polarized backscattering cross-section intensity and the VV-polarized backscattering cross-section intensity; in the normalized difference index calculation module 20, the VH-polarized backscattering cross-section intensity and the VV-polarized backscattering cross-section intensity are input into a formula , and the normalized difference index is calculated, wherein NDBI is the normalized difference index, σ VH is the VH-polarized backscattering cross-section intensity, and σ VVFor the intensity of the backscattering cross section of the VV polarization, in the rice growth status inversion module 30, the normalized difference value index is brought into the formula NDVI=a*NDBI+b to calculate the normalized vegetation index and the growth status of the rice is obtained by inversion, wherein NDVI is the normalized vegetation index, a is a coefficient, and b is a constant.

[0084] Embodiment 4

[0085] As Figure 13 shown, an image data preprocessing module 10 in a post-disaster growth status detection device of rice includes:

[0086] A study area first calculation module 11 is configured to acquire SAR images of a disaster area and contemporaneous SAR images of the disaster area, and perform water area extraction on the SAR images respectively to obtain a first water range and a second water range.

[0087] A study area second calculation module 12 is configured to remove an intersection of the first water range and the second water range to obtain a third water range.

[0088] A study area third calculation module 13 is configured to acquire a rice distribution map of the disaster area, and obtain an intersection of the rice distribution map and the third water range to obtain the study area of the disaster.

[0089] An embodiment of the above device is that, in the study area first calculation module 11, the SAR images of the disaster area and the contemporaneous SAR images of the disaster area are acquired, and the water area extraction is performed on the SAR images respectively to obtain the first water range and the second water range; in the study area second calculation module 12, the intersection of the first water range and the second water range is removed to obtain the third water range; and in the study area third calculation module 13, the rice distribution map of the disaster area is acquired, and the intersection of the rice distribution map and the third water range is obtained to obtain the study area of the disaster.

[0090] Embodiment 5

[0091] As Figure 14 shown, a rice growth status inversion module 30 in a post-disaster growth status detection device of rice includes:

[0092] A rice growth status first calculation module 31 is configured to, when the detection period is before the rice heading, bring the normalized difference value index into the formula NDVI=-2.9789*NDBI+1.2659 to calculate a first normalized vegetation index and obtain a first growth status of the rice by inversion, wherein -2.9789 is a coefficient a, and 1.2659 is a constant b.

[0093] The second rice growth condition calculation module 32 is used for detecting the rice after heading. The normalized difference index is substituted into the formula NDVI = -5.2917 * NDBI + 1.4250 to calculate and obtain the second normalized vegetation index and invert to obtain the second rice growth condition, where -5.2917 is the coefficient a and 1.4250 is the constant b.

[0094] Disaster damage classification map establishment module 33: used to obtain the normalized vegetation index that is the same as the disaster period based on the NDVI of historical rice growth, obtain a third normalized vegetation index, compare the third normalized vegetation index with the first normalized vegetation index or the second normalized vegetation index, and classify the disaster level according to a fixed threshold to establish a disaster damage classification map.

[0095] One implementation of the above device is that in the first rice growth calculation module 31, the detection period is before the rice heading, then the normalized difference index is substituted into the formula NDVI = -2.9789 * NDBI + 1.2659 for calculation, and the first normalized vegetation index is obtained and the first growth of rice is inverted, wherein -2.9789 is the coefficient a and 1.2659 is the constant b. In the second rice growth calculation module 32, the detection period is after the rice heading, then the normalized difference index is substituted into the formula NDVI = -5.2917 *NDBI+1.4250 is calculated to obtain the second normalized vegetation index and invert the second growth potential of rice, where -5.2917 is the coefficient a and 1.4250 is the constant b. In the disaster classification map establishment module 33, the normalized vegetation index same as that in the disaster period is obtained according to the NDVI of historical rice growth to obtain the third normalized vegetation index. The third normalized vegetation index is compared with the first normalized vegetation index or the second normalized vegetation index, and the disaster level is divided according to a fixed threshold to establish a disaster classification map.

[0096] Example 6

[0097] like Figure 15 As shown, an electronic device includes a memory 601 and a processor 602, wherein the memory 601 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 602 to implement any one of the above methods.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the electronic device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0099] A computer-readable storage medium storing a computer program, wherein the computer program enables a computer to implement any one of the above methods when executed.

[0100] For example, the computer program can be divided into one or more modules / units, one or more modules / units are stored in the memory 601 and executed by the processor 602, and the I / O interface transmission of data is completed by the input interface 605 and the output interface 606, so as to complete the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0101] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device can include, but is not limited to, the memory 601, the processor 602, and those skilled in the art can understand that the embodiments are only examples of the computer device, and do not constitute a limitation on the computer device, and can include more or fewer components, or combine certain components, or different components, for example, the computer device can also include an inputter 607, a network access device, a bus and the like.

[0102] The processor 602 can be a central processing unit (CPU), and can also be other general-purpose processors 602, digital signal processors (DSP) 602, application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor 602 can be a microprocessor or can also be any conventional processor.

[0103] The memory 601 can be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 601 can also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card and the like. Further, the memory 601 can include both the internal storage unit and the external storage device of the computer device. The memory 601 is used to store computer programs and other programs and data required by the computer device. The memory 601 can also be used to temporarily store the output of the outputter 608, and the aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM) 603, a random access memory (RAM) 604, a disk or an optical disk and the like.

[0104] The above merely illustrates the embodiments of the present application, but the technical features of the present application are not limited to this. Any changes or modifications made by those skilled in the art within the scope of the present application shall fall within the patent scope of the present application.

Claims

1. A method for detecting the growth of rice after a disaster, characterized in that: include: Determine a disaster-stricken study area and obtain SAR image data of the study area, perform terrain correction, radiation calibration, and filtering on the SAR image data to obtain a normalized backscatter cross-section intensity, wherein the normalized backscatter cross-section intensity includes a VH-polarized backscatter cross-section intensity and a VV-polarized backscatter cross-section intensity; Substitute the VH polarized backscattering cross-sectional intensity and the VV polarized backscattering cross-sectional intensity into the formula Calculation is performed to obtain the normalized difference index, where NDBI is the normalized difference index. is the backscattering cross section intensity of VH polarization, is the backscattering cross section intensity of VV polarization; Substitute the normalized difference index into the formula Calculation is performed to obtain the normalized vegetation index and invert it to obtain the growth of rice, where NDVI is the normalized vegetation index, a is the coefficient, and b is the constant; The affected study areas include: Acquire a SAR image of the disaster-stricken area and a SAR image of the disaster-stricken area at the same time, and extract water body areas respectively to obtain a first water body range and a second water body range; removing the intersection of the first water body range and the second water body range to obtain a third water body range; Obtaining a rice distribution map of the disaster-stricken area, and taking the intersection of the rice distribution map and the range of the third water body to obtain the disaster-stricken study area; Substituting the normalized difference index into the formula Calculations are performed to obtain the normalized vegetation index and invert the growth of rice, including: If the detection period is before rice heading, the normalized difference index is substituted into the formula Calculation is performed to obtain the first normalized vegetation index and invert it to obtain the first growth potential of rice, where -2.9789 is the coefficient a and 1.2659 is the constant b; The detection period is after rice heading, then the normalized difference index is substituted into the formula Calculation is performed to obtain the second normalized vegetation index and invert it to obtain the second growth potential of rice, where -5.2917 is the coefficient a and 1.4250 is the constant b; After the inversion obtains the growth potential of rice, the method further includes: Based on the NDVI of historical rice growth, the normalized vegetation index that is the same as that in the disaster period is obtained to obtain a third normalized vegetation index. The third normalized vegetation index is compared with the first normalized vegetation index or the second normalized vegetation index, and the disaster level is divided according to a fixed threshold to establish a disaster damage classification map.

2. A rice growth detection device after a disaster, used to implement the rice growth detection method after a disaster as claimed in claim 1, characterized in that: include: Image data preprocessing module: used to determine the affected study area and obtain SAR image data of the study area, perform terrain correction, radiation calibration, and filtering on the SAR image data to obtain normalized backscatter cross-sectional intensity, which includes VH polarization backscatter cross-sectional intensity and VV polarization backscatter cross-sectional intensity; Normalized difference index calculation module: used to bring the backscattering cross-section intensity of the VH polarization and the backscattering cross-section intensity of the VV polarization into the formula Calculation is performed to obtain the normalized difference index, where NDBI is the normalized difference index. is the backscattering cross section intensity of VH polarization, is the backscattering cross section intensity of VV polarization; Rice growth inversion module: used to bring the normalized difference index into the formula Calculations are performed to obtain the normalized vegetation index and invert it to obtain the growth of rice, where NDVI is the normalized vegetation index, a is the coefficient, and b is a constant.

3. The device for detecting the growth of rice after a disaster according to claim 2, characterized in that: The image data preprocessing module includes: The first calculation module of the study area is used to obtain the SAR image of the disaster-stricken area and the SAR image of the disaster-stricken area at the same time, and extract the water body area respectively to obtain the first water body range and the second water body range; A second calculation module for the study area is used to remove the intersection of the first water body range and the second water body range to obtain a third water body range; The third calculation module of the study area is used to obtain the rice distribution map of the disaster-stricken area, take the intersection of the rice distribution map and the third water body range, and obtain the disaster-stricken study area.

4. The device for detecting the growth of rice after a disaster according to claim 2, characterized in that: The rice growth inversion module includes: The first calculation module of rice growth: used for detecting the period before rice heading, then the normalized difference index is substituted into the formula Calculation is performed to obtain the first normalized vegetation index and invert it to obtain the first growth potential of rice, where -2.9789 is the coefficient a and 1.2659 is the constant b; The second calculation module for rice growth: used for detecting the period after rice heading, then the normalized difference index is substituted into the formula The second normalized vegetation index was calculated and the second growth potential of rice was obtained by inversion, where -5.2917 is the coefficient a and 1.4250 is the constant b.

5. The device for detecting the growth of rice after a disaster according to claim 4, characterized in that: The rice growth inversion module further includes: Disaster damage classification map establishment module: used to obtain the same normalized vegetation index as the disaster period based on the NDVI of historical rice growth, obtain the third normalized vegetation index, compare the third normalized vegetation index with the first normalized vegetation index or the second normalized vegetation index, and divide the disaster level according to a fixed threshold to establish a disaster damage classification map.

6. A device for detecting the growth of rice after a disaster, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method for detecting the growth of rice after a disaster as claimed in claim 1.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a computer, the method for detecting the growth condition of rice after a disaster as claimed in claim 1 is implemented.

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