CN113609910A - Winter wheat growth early-stage spatial distribution monitoring method and device based on remote sensing image - Google Patents
Winter wheat growth early-stage spatial distribution monitoring method and device based on remote sensing image Download PDFInfo
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Abstract
The invention provides a method and a device for monitoring spatial distribution of winter wheat in an early growth stage based on remote sensing images, wherein the method comprises the following steps: acquiring a satellite remote sensing image of a target area; determining an NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image; and determining a winter wheat distribution area based on the NDSVI value and a preset threshold value of the H value according to the NDSVI image and the H image. According to the method, the spatial distribution monitoring of the wheat in the early growth stage is carried out through the NDSVI value and the H value threshold of the remote sensing image, the dependence on multi-temporal remote sensing image data is greatly reduced, the precision error interference caused by multi-temporal image correction is reduced, and the spatial distribution monitoring is realized under the weak vegetation signal of the winter wheat in the early growth stage. The method aims at the problems that plants are weak and small in early growth stage of the winter wheat, vegetation is sparse, and spectral information of remote sensing images is easily influenced by soil background, and achieves quick and accurate remote sensing monitoring of spatial distribution in the early growth stage of the winter wheat.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a method and a device for monitoring spatial distribution of winter wheat in the early growth stage based on remote sensing images.
Background
Winter wheat is the most widely distributed main grain crop in northern areas of China, and the spatial distribution condition and the planting area of the winter wheat are important information to be mastered by relevant departments such as agriculture and the like. The spatial distribution condition of the winter wheat is accurately extracted as early as possible, and information support can be provided for field management, pest control, yield prediction and the like of the winter wheat in winter and in the middle and later growth periods. The traditional winter wheat spatial distribution condition monitoring is mainly carried out in a field investigation mode, a large amount of manpower, material resources and financial resources are consumed, the labor intensity is high, the progress is slow, the efficiency is low, the investigation range is limited, the interference of human factors is easy to occur, and the precision is low.
In recent years, remote sensing technology is rapidly developed, and a plurality of scholars realize the remote sensing monitoring of spatial distribution and area of winter wheat in a large area in the middle and later growth periods of wheat with dense plants and strong vegetation signals or by utilizing multi-temporal remote sensing images in the whole growth period of the wheat. However, the method using multi-temporal images has high requirements for spatial registration, radiation correction and the like of image data, and can be completed only by images of multiple temporal phases, and the methods generally can meet the precision requirement in the middle and later growth periods of winter wheat, and cannot meet the requirements of production departments on field water and fertilizer management, pest control and yield estimation in the early growth period of winter wheat in winter.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for monitoring spatial distribution of winter wheat in the early growth stage based on remote sensing images.
The invention provides a winter wheat early growth spatial distribution monitoring method based on remote sensing images, which is based on the remote sensing images and comprises the following steps: acquiring a satellite remote sensing image of a target area to be monitored; determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image; and determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
According to the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image, the step of determining the normalized short wave infrared vegetation index NDSVI image according to the remote sensing image comprises the following steps: and (3) operating the B9 and B12 wave bands by using a wave band calculation tool on the preprocessed sentinel second satellite image according to an NDSVI calculation formula to generate a normalized short-wave infrared vegetation index NDSVI image.
According to the winter wheat growth early-stage spatial distribution monitoring method based on the remote sensing image, the satellite remote sensing image of the target area to be monitored is acquired, and the method comprises the following steps: and acquiring a remote sensing image of a sentinel second satellite at the early season of the current growth of the target area to be monitored.
According to the method for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image, the determination of the HSV color space H image according to the remote sensing image comprises the following steps: respectively taking the SWIR1 waveband reflectance value, the near infrared waveband reflectance value and the red waveband reflectance value as three channel values of an RGB space to obtain an RGB image; and according to the RGB image, converting the color space into an HSV channel, extracting hue H, and generating an HSV color space H image.
According to the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image, according to the NDSVI image and the H image, before determining the winter wheat distribution area based on the preset threshold of the NDSVI value and the H value, the method further comprises the following steps: selecting a plurality of wheat sampling points and a plurality of non-wheat sampling points, and determining pixel values corresponding to the H image and the NDSVI image for the wheat sampling points and the non-wheat sampling points; and determining the threshold value of the boundary of the wheat sampling point and the non-wheat sampling point as the preset threshold value according to the values of the wheat sampling point and the non-wheat sampling point.
According to the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image, H is greater than 71 degrees, and NDSVI is less than-0.21, and the region is the winter wheat region.
According to the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image, after the winter wheat distribution area is determined, the method further comprises the following steps: and converting the binary image with the partitioning result into a shp-format vector diagram, and displaying the shp-format vector diagram in a superposition manner with the remote sensing image.
The invention also provides a device for monitoring the spatial distribution of winter wheat in the early growth stage based on the remote sensing image, which comprises: the acquisition module is used for acquiring a satellite remote sensing image of a target area to be monitored; the processing module is used for determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image and determining an HSV color space H image according to the remote sensing image; and the dividing module is used for determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of any one of the methods for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image.
The invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for monitoring spatial distribution of winter wheat growth early stage based on remote sensing images as described in any one of the above.
According to the winter wheat early growth period space distribution monitoring method and device based on the remote sensing images, on the basis of the NDSVI images and the H images, wheat is partitioned through the NDSVI values and the H value threshold values, dependence on multi-temporal remote sensing image data is greatly reduced, and precision error interference caused by multi-temporal image correction is reduced, so that accurate winter wheat distribution results can be obtained only through single-scene satellite remote sensing images, the situation that the precision requirements can be met only in the middle and later growth periods of winter wheat is avoided, early winter wheat monitoring is achieved in the early growth period, and further the requirements of a production department on water and fertilizer management, pest control and yield estimation in the winter wheat field in the early growth period are met. The method aims at the problems that plants are weak and small in early growth stage of the winter wheat, vegetation is sparse, and spectral information of remote sensing images is easily influenced by soil background, and achieves remote sensing rapid and accurate monitoring of spatial distribution of the winter wheat in the early growth stage.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method for monitoring spatial distribution of winter wheat in an early growth stage based on remote sensing images, provided by the invention;
FIG. 2 is a graph of the main feature spectrum provided by the present invention;
FIG. 3 is a second schematic flow chart of the method for monitoring spatial distribution of winter wheat in the early stage of growth based on remote sensing images according to the present invention;
FIG. 4 is a schematic structural diagram of a device for monitoring spatial distribution of winter wheat in an early growth stage based on remote sensing images, provided by the invention;
fig. 5 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method and the device for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image are described below with reference to fig. 1 to 5. Fig. 1 is one of the flow diagrams of the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image, and as shown in fig. 1, the winter wheat early growth period spatial distribution monitoring method based on the remote sensing image provided by the invention comprises the following steps:
101. and acquiring a satellite remote sensing image of a target area to be monitored.
For example, a satellite remote sensing image of a target area to be monitored for 11 months and 8 days is obtained from an European office official network (https:// scihub. And then preprocessing the image, including data decompression, data derivation, band combination, radiation correction, geometric correction and image clipping.
102. And determining an NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image.
The wheat seedling-raising method can be used for division from multiple angles in the early growth stage, division is carried out in the growth period, and wheat can be sowed from seedling emergence to the overwintering stage. Firstly, uniformly selecting main ground feature sample points in the current season field of winter wheat in the early growth stage, wherein 62 sample points of the winter wheat, 63 sample points of bare land, 90 sample points of corn straws and 65 sample points of deciduous forest land are selected. Then, the pixel values of 12 wave bands of B1-B12 are extracted from winter wheat, bare land, corn stalk and deciduous forest land in the image of day 8 and day 11, so as to form the spectrum curves of the 4 land features, as shown in FIG. 2. In order to embody details, the reflectivity is enlarged by 10000 times, and then NDSVI index calculation is performed.
According to the spectrum curve chart of main land features of the current-season farmland in the early growth stage of winter wheat, the spectrum differences of the winter wheat, bare land, field corn stalks and fallen leaf forest land are comprehensively analyzed, and the following are found: the reflectivity of winter wheat is lowest in a B12 wave band, the reflectivity is higher in a B9 wave band, and the difference value of the two wave bands is largest, so that the winter wheat can be effectively distinguished from other ground objects by utilizing the normalized short-wave infrared vegetation index NDSVI. The normalized short-wave infrared vegetation index NDSVI is as follows:
in the above formula, B9 is the reflectance value of B9 band in the sentinel second satellite image, and B12 is the reflectance value of B12 band in the sentinel second satellite image.
In ENVI software, a wave band calculation tool is used for calculating B9 and B12 wave bands according to an NDSVI calculation formula on the preprocessed sentinel second satellite image, and a normalized short-wave infrared vegetation index NDSVI image is generated.
The RGB model and HSV model are two more common color models. The RGB model is a three-dimensional cubic model, and R, G, B three-color lights are superimposed on each other to realize color mixing, wherein the three coordinate components R, G, B three-color lights have value ranges of [0, 255 ]. The HSV model is a cone model formed by erecting and flattening the central axis of the three-dimensional coordinate of the RGB cube model, wherein three coordinate components are hue (H), saturation (S) and lightness (V). H represents an angle, and the value range is [0 degrees, 360 degrees ]; s represents the color depth, and the value range is [0, 1 ]; v represents the color brightness degree, and the value range is [0, 1 ]. Compared with an RGB model, the HSV model emphasizes color representation, is more relevant to a color perception mode of human eyes, and is more in line with natural sense of a human visual system. The color space transformation formula from the RGB model to the HSV model is as follows:
V＝max(R,G,B)
in order to enhance the signal intensity of the vegetation and improve the extraction precision of the winter wheat in the early stage of growth, the color space is converted from the RGB channel to the HSV channel through a color conversion tool, the hue (H), the saturation (S) and the brightness (V) are extracted, and an H image, an S image and a V image are respectively generated. This step may be performed in the ENVI software.
103. And determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
According to the NDSVI images and the H images of the wheat areas and the non-wheat areas, historical data can be analyzed to obtain an NDSVI index value threshold value and an H index value threshold value which clearly distinguish the NDSVI index value threshold value and the H index value threshold value, and the images are partitioned based on the two threshold values to obtain the distribution of the wheat areas and the non-wheat areas.
According to the winter wheat early-stage growth spatial distribution monitoring method based on the remote sensing images, on the basis of the NDSVI images and the H images, wheat is partitioned through the NDSVI values and the H value threshold values, dependence on multi-temporal remote sensing image data is greatly reduced, and precision error interference caused by multi-temporal image correction is reduced, so that accurate winter wheat distribution results can be obtained only through single-scene satellite remote sensing images, the condition that the precision requirement can be met in the middle and later stages of winter wheat growth is avoided, early winter wheat monitoring is realized in the early stage of growth, and further, the method is beneficial to the requirements of a production department on water and fertilizer management, pest control and yield estimation in the winter wheat field in the early stage of growth. The method aims at the problems that plants are weak and small in early growth stage of the winter wheat, vegetation is sparse, and spectral information of remote sensing images is easily influenced by soil background, and achieves remote sensing rapid and accurate monitoring of spatial distribution of the winter wheat in the early growth stage.
In one embodiment, the determining a normalized short-wave infrared vegetation index NDSVI image from the remote sensing image includes: and (3) operating the B9 and B12 wave bands by using a wave band calculation tool on the preprocessed sentinel second satellite image according to an NDSVI calculation formula to generate a normalized short-wave infrared vegetation index NDSVI image. The above embodiments have been specifically described, and reference is made specifically to the above embodiments.
In one embodiment, the acquiring a satellite remote sensing image of a target area to be monitored includes: and acquiring a remote sensing image of a sentinel second satellite at the early season of the current growth of the target area to be monitored.
The sentinel second image has 12 wave bands of B1(443.9nm), B2(496.6nm), B3(560.0nm), B4(664.5nm), B5(703.9nm), B6(740.2nm), B7(782.5nm), B8(835.1nm), B8B (864.8nm), B9(945.0nm), B11(1613.7nm) and B12(2202.4nm), the data decompression and data derivation of the image can be completed in SNAP software specified by the European space, the wave band combination, radiation correction, geometric correction and image cutting of the image can be completed in ENVI software, the image spatial resolution is 10m, and the coordinate system is WGS84_ UTM _ Zone 50N.
In one embodiment, the determining HSV color space H image from the remote-sensing image includes: respectively taking the SWIR1 waveband reflectance value, the near infrared waveband reflectance value and the red waveband reflectance value as three channel values of an RGB space to obtain an RGB image; and according to the RGB image, converting the color space into an HSV channel, extracting hue H, and generating an HSV color space H image.
According to comprehensive analysis, the winter wheat is different from other ground objects in RED wave band RED (B4), near infrared wave band NIR (B8) and short wave infrared 1 wave band SWIR1(B11), so that the SWIR1(B11), the NIR (B8) and the RED (B4) three wave bands are selected as RGB channels.
In order to enhance the signal intensity of the vegetation and improve the extraction precision of the winter wheat in the early stage of growth, the color space is converted from the RGB channel to the HSV channel through a color conversion tool, the hue (H), the saturation (S) and the brightness (V) are extracted, and an H image, an S image and a V image are respectively generated. This step may be performed in the ENVI software.
According to the winter wheat early-stage growth spatial distribution monitoring method based on the remote sensing image, the vegetation signals are enhanced by adopting HSV color space transformation, and the normalized short-wave infrared vegetation index NDSVI sensitive to soil and vegetation information differentiation is integrated, so that the winter wheat early-stage growth spatial distribution remote sensing rapid and accurate monitoring can be realized.
In one embodiment, before determining the winter wheat distribution region based on the predetermined threshold of the NDSVI value and the H value according to the NDSVI image and the H image, the method further includes: selecting a plurality of wheat sampling points and a plurality of non-wheat sampling points, and determining pixel values corresponding to the H image and the NDSVI image for the wheat sampling points and the non-wheat sampling points; and determining the threshold value of the boundary of the wheat sampling point and the non-wheat sampling point as the preset threshold value according to the values of the wheat sampling point and the non-wheat sampling point.
Selecting 100 wheat sampling points and 100 nude non-wheat sampling points, extracting the pixel values of the wheat sampling points and the non-wheat sampling points corresponding to the two-waveband H-NDSVI images through an 'extraction value to point tool', and making H-NDSVI scatter diagrams of the wheat sampling points and the non-wheat sampling points. And (4) visually screening the wheat and non-wheat difference boundary according to the scatter diagram.
In one embodiment, H > 71, and NDSVI < -0.21 is a winter wheat region.
Threshold analysis shows that the H value of non-wheat sample points is generally less than 68 degrees, and the NDSVI value is generally greater than-0.18; the H value of the winter wheat sampling point is generally more than 73 degrees, and the NDSVI value is generally less than-0.24, so that the optimal threshold value for extracting the winter wheat in the current season in the early stage of growth is determined to be H more than 71 degrees and NDSVI less than-0.21 according to the intermediate processing principle.
The specific rule set of remote sensing extraction of winter wheat in the current season in the early growth stage is as follows:
(1) tone H image: when H is more than 71 degrees, the wheat is used, and when H is less than or equal to 71 degrees, the wheat is not used.
(2) Normalizing the short-wave infrared vegetation index NDSVI image: when the NDSVI is less than-0.21, the wheat is used, and when the NDSVI is more than or equal to-0.21, the non-wheat is used.
In one embodiment, after the determining the distribution area of the winter wheat, the method further comprises: converting the binary image with the partitioning result into a shp-format vector diagram; and displaying the remote sensing image in an overlapping manner.
Based on the rules and the threshold value of the rule set, assigning the region with the angle of H & gt 71 degrees in the hue H image as 1 operation, and assigning the region with the angle of H & lt or equal to 71 degrees in the hue H image as 0 operation to generate a hue H binary image; and assigning the area of NDSVI less than-0.21 in the normalized short-wave infrared vegetation index NDSVI image as 1 operation, assigning the area of not less than-0.21 in the normalized short-wave infrared vegetation index NDSVI image as 0 operation, and generating a normalized short-wave infrared vegetation index NDSVI binary image.
And then, carrying out superposition analysis on the hue H binary image and the normalized short-wave infrared vegetation index NDSVI binary image to generate an extracted binary image of the winter wheat in the current season in the early growth stage, wherein the region with the value of 1 is a winter wheat distribution region. This step can be done in ArcGIS software.
The binary image extracted from the winter wheat in the early stage of growth generated in the previous step is a grid image, and in order to facilitate later-stage practical application, the binary image (grid image) extracted from the winter wheat in the early stage of growth is converted into a map (vector file) of the winter wheat in the early stage of growth in a shp format by a grid-to-vector tool. This step can be done in ArcGIS software.
And overlapping and displaying the generated current season winter wheat distribution map (shp format) in the early growth stage and the remote sensing image II of the sentinel, and drawing to generate a final current season winter wheat distribution thematic map in the early growth stage. This step can be done in ArcGIS software.
Fig. 3 is a second schematic flow chart of the method for monitoring spatial distribution of winter wheat in the early growth stage based on remote sensing images, which comprises the following steps:
step 1) acquiring and preprocessing a single-scene satellite remote sensing image at the early stage of winter wheat growth in the current season;
step 2), generating a normalized short-wave infrared vegetation index NDSVI;
step 3) color space transformation from RGB to HSV;
step 4), generating two-waveband images (H-NDSVI);
step 5) extracting the best threshold value analysis of winter wheat in the current season in the early growth stage;
step 6), performing distributed extraction on winter wheat in the current season in the early growth stage;
step 7), converting the extracted result raster image of the winter wheat in the current season in the early growth stage into a vector file;
and 8) making a winter wheat distribution map in the current season in the early growth stage.
The description may be specifically made in conjunction with the above embodiments, and is not repeated here.
The device for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image provided by the invention is described below, and the device for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image described below and the method for monitoring the spatial distribution of the winter wheat in the early growth stage based on the remote sensing image described above can be correspondingly referred to each other.
Fig. 4 is a schematic structural diagram of the device for monitoring spatial distribution of winter wheat in the early stage of growth based on remote sensing images, as shown in fig. 4, the device for monitoring spatial distribution of winter wheat in the early stage of growth based on remote sensing images comprises: an acquisition module 401, a processing module 402 and a partitioning module 403. The acquisition module 401 is configured to acquire a satellite remote sensing image of a target area to be monitored; the processing module 402 is configured to determine a normalized short-wave infrared vegetation index NDSVI image according to the remote sensing image, and determine an HSV color space H image according to the remote sensing image; the dividing module 403 is configured to determine a winter wheat distribution area based on a preset threshold of the NDSVI value and the H value according to the NDSVI image and the H image.
The device embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the details, reference is made to the above method embodiments, which are not described herein again.
According to the winter wheat early-stage growth spatial distribution monitoring device based on the remote sensing image, provided by the embodiment of the invention, on the basis of the NDSVI image and the H image, wheat is partitioned through the NDSVI value and the H value threshold, so that the dependence on multi-temporal remote sensing image data is greatly reduced, and the precision error interference caused by multi-temporal image correction is reduced, so that an accurate winter wheat distribution result can be obtained only through a single-scene satellite remote sensing image, the condition that the precision requirement can be met in the middle and later stages of winter wheat growth is avoided, early monitoring of the winter wheat is realized in the early stage of growth, and further, the device is favorable for the production department to meet the requirements of water and fertilizer management, pest control and early estimation of the wheat in the winter wheat field in the early stage of growth. The method aims at the problems that plants are weak and small in early growth stage of the winter wheat, vegetation is sparse, and spectral information of remote sensing images is easily influenced by soil background, and achieves remote sensing rapid and accurate monitoring of spatial distribution of the winter wheat in the early growth stage.
Fig. 5 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 5, the electronic device may include: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a communication bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the communication bus 504. The processor 501 can call the logic instructions in the memory 503 to execute the remote sensing image-based winter wheat growth early-stage spatial distribution monitoring method, which comprises the following steps: acquiring a satellite remote sensing image of a target area to be monitored; determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image; and determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
In addition, the logic instructions in the memory 503 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the remote sensing image-based winter wheat pre-growth period spatial distribution monitoring method provided by the above methods, the method including: acquiring a satellite remote sensing image of a target area to be monitored; determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image; and determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
In another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method for monitoring spatial distribution of winter wheat pre-growth period based on remote sensing images provided in the foregoing embodiments, the method including: acquiring a satellite remote sensing image of a target area to be monitored; determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image; and determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A winter wheat growth early-stage spatial distribution monitoring method based on remote sensing images is characterized by comprising the following steps:
acquiring a satellite remote sensing image of a target area to be monitored;
determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image, and determining an HSV color space H image according to the remote sensing image;
and determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
2. The winter wheat early growth stage spatial distribution monitoring method based on the remote sensing image according to claim 1, wherein the determining a normalized short wave infrared vegetation index (NDSVI) image according to the remote sensing image comprises:
and (3) operating the B9 and B12 wave bands by using a wave band calculation tool on the preprocessed sentinel second satellite image according to an NDSVI calculation formula to generate a normalized short-wave infrared vegetation index NDSVI image.
3. The remote sensing image-based winter wheat early growth period space distribution monitoring method according to claim 1, wherein the obtaining of the satellite remote sensing image of the target area to be monitored comprises:
and acquiring a remote sensing image of a sentinel second satellite at the early season of the current growth of the target area to be monitored.
4. The method for monitoring spatial distribution of winter wheat in the early stage of growth based on remote sensing images as claimed in claim 3, wherein determining HSV color space H images from said remote sensing images comprises:
respectively taking the SWIR1 waveband reflectance value, the near infrared waveband reflectance value and the red waveband reflectance value as three channel values of an RGB space to obtain an RGB image;
and according to the RGB image, converting the color space into an HSV channel, extracting hue H, and generating an HSV color space H image.
5. The remote sensing image-based winter wheat early growth stage spatial distribution monitoring method of claim 1, wherein before determining a winter wheat distribution region based on preset threshold values of NDSVI values and H values according to the NDSVI image and H image, the method further comprises:
selecting a plurality of wheat sampling points and a plurality of non-wheat sampling points, and determining pixel values corresponding to the H image and the NDSVI image for the wheat sampling points and the non-wheat sampling points;
and determining the threshold value of the boundary of the wheat sampling point and the non-wheat sampling point as the preset threshold value according to the values of the wheat sampling point and the non-wheat sampling point.
6. The winter wheat early-growth-stage spatial distribution monitoring method based on remote sensing images as claimed in claim 4, wherein H > 71 degrees and NDSVI < -0.21 is a winter wheat region.
7. The remote sensing image-based winter wheat early growth stage spatial distribution monitoring method according to claim 1, wherein after determining the winter wheat distribution region, further comprising:
converting the binary image with the partitioning result into a shp-format vector diagram;
and displaying the remote sensing image in an overlapping manner.
8. The utility model provides a winter wheat growth prophase space distribution monitoring devices based on remote sensing image which characterized in that includes:
the acquisition module is used for acquiring a satellite remote sensing image of a target area to be monitored;
the processing module is used for determining a normalized short wave infrared vegetation index NDSVI image according to the remote sensing image and determining an HSV color space H image according to the remote sensing image;
and the dividing module is used for determining a winter wheat distribution area based on a preset threshold value of the NDSVI value and the H value according to the NDSVI image and the H image.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the remote sensing image-based winter wheat pre-growth period spatial distribution monitoring method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the remote sensing image-based winter wheat pre-growth spatial distribution monitoring method according to any one of claims 1 to 7.
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