Greenhouse identification method, device, electronic device and computer-readable storage medium

Abstract

The present invention provides a greenhouse identification method, device, electronic device, and computer-readable storage medium, relating to the field of remote sensing image processing technology. When performing greenhouse identification, the present invention first obtains raw remote sensing image data of a target area, the raw remote sensing image data including reflectance data in a preset band; then preprocesses the raw remote sensing image data to obtain target remote sensing image data; and then performs greenhouse identification on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result for the target area. Using the modified greenhouse index (MPGI) to perform greenhouse identification on the preprocessed target remote sensing image data takes into account the differences between greenhouses and non-greenhouses in the preset band, and uses initial discrimination parameters to enhance greenhouse identification and suppress non-greenhouse type information, thereby improving greenhouse identification accuracy and effectiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a greenhouse identification method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] Over the past few decades, plastic greenhouses have rapidly transformed agricultural practices around the world. As an effective modern agricultural method, greenhouses protect crops from unstable environments and provide off-season crops, making them widely used in the agricultural sector. Although plastic greenhouses can produce high-quality crops, their widespread use has also created several environmental problems. First, greenhouses generate plastic waste, leading to soil degradation and water pollution. Second, intensive agricultural practices alter land cover, further impacting landscapes and ecosystem services. Finally, greenhouses affect landscape aesthetics, such as reduced visual clarity and decreased regional biodiversity. Therefore, accurately mapping the spatiotemporal distribution of greenhouses is essential for environmental monitoring and agricultural policy implementation. The increasing availability of remote sensing data and the maturity of analytical methods have made remote sensing technology widely used to monitor land cover, providing the potential for identifying greenhouses.

[0003] There are two types of greenhouse identification methods using remote sensing: one is the feature extraction method that uses land cover type characteristics, such as using object-based image analysis and decision tree classification to detect greenhouses; the other is the index-based extraction method, such as using the vegetable index (VegetationIndex, abbreviated as VI, or V I ) to extract greenhouses. However, feature extraction methods require high-spatial-resolution imagery (e.g., greenhouse identification based on images with a spatial resolution of 0.5 m). This type of imagery is difficult to obtain, lacks temporal and spatial continuity, and requires a large number of training samples. This results in poor feature extraction for large-scale identification. Index-based extraction methods are particularly prone to the problems of different objects with the same spectrum and different spectra for the same object, resulting in poor recognition results. Summary of the Invention

[0004] The object of the present invention is to provide a greenhouse identification method, device, electronic device and computer-readable storage medium to improve the greenhouse identification accuracy and recognition effect.

[0005] In a first aspect, an embodiment of the present invention provides a greenhouse identification method, comprising:

[0006] Acquire original remote sensing image data of the target area, wherein the original remote sensing image data includes reflectance data of preset bands, wherein the preset bands include a coastal aerosol band, a blue band, a green band, a near infrared band, a shortwave infrared 1, and a shortwave infrared 2;

[0007] Preprocessing the original remote sensing image data to obtain target remote sensing image data;

[0008] Greenhouse identification is performed on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result for the target area; wherein the MPGI is defined as follows:

[0009]

[0010] v=(R Green -R Blue )×(R NIR -R SWIR1 -0.02);

[0011] Among them, R coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of short-wave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, and v is the preset initial discrimination parameter.

[0012] Furthermore, the preprocessing of the original remote sensing image data to obtain target remote sensing image data includes:

[0013] Performing radiometric calibration, atmospheric correction, and cropping of the target area on the original remote sensing image data to obtain processed data;

[0014] Typical land object types are removed from the processed data to obtain target remote sensing image data; wherein the typical land object types include one or more of forests, water bodies, snow and bare soil.

[0015] Furthermore, the process of removing typical land feature types from the processed data to obtain target remote sensing image data includes:

[0016] Identifying the typical land feature type on the processed data based on the normalized vegetation index, the improved water body index, and the normalized difference bare soil index to obtain a non-greenhouse identification result;

[0017] The non-greenhouse area data corresponding to the non-greenhouse identification result is eliminated from the processed data to obtain target remote sensing image data.

[0018] Furthermore, the greenhouse identification is performed on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain the target greenhouse identification result of the target area, including:

[0019] Traversing each pixel in the target remote sensing image data;

[0020] For the traversed current pixel, based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, the MPGI data corresponding to the current pixel is calculated, and the greenhouse identification result of the current pixel is determined based on the MPGI data; wherein the MPGI data includes an initial discrimination parameter value and an MPGI value;

[0021] Until all pixels in the target remote sensing image data are traversed, the target greenhouse recognition result of the target area is obtained.

[0022] Furthermore, the calculating of the MPGI data corresponding to the current pixel based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, and determining the greenhouse recognition result of the current pixel based on the MPGI data, includes:

[0023] Based on the first reflectivity data corresponding to the current pixel in the target remote sensing image data, an initial discrimination parameter value corresponding to the current pixel is calculated; wherein the first reflectivity data includes a reflectivity value of a blue band, a reflectivity value of a green band, a reflectivity value of a near-infrared band, and a reflectivity value of a short-wave infrared 1;

[0024] Determine whether the initial discrimination parameter value corresponding to the current pixel is greater than 0, and obtain a first judgment result;

[0025] When the first judgment result is no, determining that the current pixel belongs to a non-greenhouse area;

[0026] When the first judgment result is yes, the MPGI value corresponding to the current pixel is calculated based on the second reflectivity data corresponding to the current pixel in the target remote sensing image data; wherein the second reflectivity data includes the reflectivity value of the coastal aerosol band, the reflectivity value of the blue band, the reflectivity value of the near-infrared band, the reflectivity value of the shortwave infrared 1, and the reflectivity value of the shortwave infrared 2;

[0027] Determine whether the MPGI value corresponding to the current pixel is greater than or equal to a preset MPGI threshold, and obtain a second determination result;

[0028] When the second judgment result is yes, determining that the current pixel belongs to the greenhouse area;

[0029] When the second judgment result is no, it is determined that the current pixel belongs to a non-greenhouse area.

[0030] Furthermore, after performing greenhouse identification on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain a target greenhouse identification result of the target area, the greenhouse identification method further includes:

[0031] Based on the target greenhouse identification result, a greenhouse distribution map of the target area is generated.

[0032] Furthermore, after performing greenhouse identification on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain a target greenhouse identification result of the target area, the greenhouse identification method further includes:

[0033] Based on the target greenhouse identification result, the greenhouse ratio value of the target area is calculated.

[0034] In a second aspect, an embodiment of the present invention further provides a greenhouse identification device, comprising:

[0035] A data acquisition module is used to acquire original remote sensing image data of a target area, wherein the original remote sensing image data includes reflectance data of preset bands, wherein the preset bands include a coastal aerosol band, a blue band, a green band, a near infrared band, a shortwave infrared 1, and a shortwave infrared 2;

[0036] A preprocessing module, configured to preprocess the original remote sensing image data to obtain target remote sensing image data;

[0037] The greenhouse identification module is used to perform greenhouse identification on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result of the target area; wherein the MPGI is defined as follows:

[0038]

[0039] v=(R Green -R Blue )×(R NIR -R SWIR1 -0.02);

[0040] Among them, R coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of short-wave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, and v is the preset initial discrimination parameter.

[0041] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the greenhouse identification method described in the first aspect is implemented.

[0042] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the greenhouse identification method described in the first aspect is executed.

[0043] The greenhouse identification method, device, electronic device, and computer-readable storage medium provided by the embodiments of the present invention first obtain raw remote sensing image data of the target area when performing greenhouse identification. The raw remote sensing image data includes reflectance data of preset bands, including the coastal aerosol band, the blue band, the green band, the near-infrared band, the shortwave infrared 1, and the shortwave infrared 2. The raw remote sensing image data is then preprocessed to obtain target remote sensing image data. Then, greenhouse identification is performed on the target remote sensing image data based on a preset modified greenhouse index (MPGI), obtaining a target greenhouse identification result for the target area. Using the modified greenhouse index (MPGI) to perform greenhouse identification on the preprocessed target remote sensing image data takes into account the differences between greenhouses and non-greenhouses in the preset bands, and uses initial discriminant parameters to enhance greenhouse identification and suppress non-greenhouse type information, thereby improving greenhouse identification accuracy and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A schematic flow chart of a greenhouse identification method provided by an embodiment of the present invention;

[0046] Figure 2 A schematic flow chart of another greenhouse identification method provided by an embodiment of the present invention;

[0047] Figure 3 Reflectivity curves of various ground object types in different bands provided by the embodiment of the present invention;

[0048] Figure 4 Identification results corresponding to various greenhouse indexes provided in the embodiment of the present invention;

[0049] Figure 5The F1 values of various greenhouse indices provided in the embodiment of the present invention at different thresholds;

[0050] Figure 6 A schematic structural diagram of a greenhouse identification device provided by an embodiment of the present invention;

[0051] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] For greenhouse identification, unlike feature extraction methods, index-based extraction methods have low computational cost, fast extraction speed, and no data training required. However, due to the similar reflectance between mixed pixels and greenhouse pixels, mixed land cover pixels pose a challenge for land use classification. Mixed pixels refer to pixels that, due to the limited spatial resolution of sensors and the spatial heterogeneity of surface cover, often do not fully correspond to a single ground feature type, but instead contain mixed information from multiple ground feature types.

[0054] Currently, existing greenhouse identification methods have little research on fragmented areas. The problems of different objects with the same spectrum and different spectra with the same object are particularly prominent. The existing greenhouse index has low recognition accuracy in fragmented areas. Among them, fragmented areas refer to areas where large natural landscapes are divided into many small pieces due to human activities or other reasons. Fragmented terrain requires higher recognition accuracy. Based on this, the embodiments of the present invention provide a greenhouse identification method, device, electronic device and computer-readable storage medium. Based on the spectral information of the surface feature type spectrum (i.e., the reflectivity of multiple surface feature types in different bands) in different bands, a modified greenhouse index (MPGI) suitable for fragmented areas is proposed. Compared with the existing greenhouse index, it has higher accuracy and better extraction results; it has strong adaptability and higher accuracy in different time and images; before using the MPGI index, a separation judgment method is proposed (i.e., using v>0 for preliminary judgment of greenhouses) to enhance the recognition of greenhouses and suppress non-greenhouse type information.

[0055] It should be noted that the embodiments of the present invention are particularly suitable for fragmented terrain, and are more suitable for other terrains such as continuous terrain.

[0056] To facilitate understanding of this embodiment, a greenhouse identification method disclosed in an embodiment of the present invention is first introduced in detail.

[0057] The embodiment of the present invention provides a greenhouse identification method, which can be executed by an electronic device based on data processing capabilities. Figure 1 The flowchart of a greenhouse identification method shown in FIG. 1 mainly includes the following steps S110 to S130:

[0058] Step S110 , obtaining original remote sensing image data of the target area, where the original remote sensing image data includes reflectivity data of a preset band.

[0059] Among them, the preset bands include coastal aerosol band B1, blue band B2, green band B3, near infrared band B5, shortwave infrared 1 (SWIR1) B6 and shortwave infrared 2 (SWIR2) B7, and can also include red band B4, etc.

[0060] The original remote sensing image data can be, but is not limited to, image data covering the target area collected by the Landsat 8 satellite's Operational Land Imager (OLI), with a spatial resolution of 30 meters. Different bands (e.g., B1, B2, B3, B4, B5, B6, and B7) represent signals received by the OLI sensor in different regions of the electromagnetic spectrum. The specific bands are as follows:

[0061] B1: Coastal aerosol band, with a wavelength range of approximately 0.43 to 0.45 μm, is used to detect coastal waters and atmospheric aerosols.

[0062] B2: Blue band, with a wavelength range of approximately 0.45 to 0.51 μm, is suitable for water detection and distinguishing water from ground objects.

[0063] B3: Green band, wavelength range is about 0.53 to 0.59 μm, used for vegetation monitoring.

[0064] B4: Red band, with a wavelength range of approximately 0.64 to 0.67 μm, is commonly used in agriculture and vegetation health assessment.

[0065] B5: Near-infrared band, with a wavelength range of approximately 0.85 to 0.88 μm, used for measuring vegetation coverage.

[0066] B6: Shortwave infrared 1 (SWIR1), with a wavelength range of approximately 1.57 to 1.65 μm, suitable for detecting soil moisture and mineral types.

[0067] B7: Shortwave infrared 2 (SWIR2), with a wavelength range of approximately 2.11 to 2.29 μm, is used for the identification of rocks and minerals.

[0068] Step S120 , preprocessing the original remote sensing image data to obtain target remote sensing image data.

[0069] To accurately extract greenhouse information, the raw remote sensing image data can be preprocessed before greenhouse identification. In some possible embodiments, step S120 can be implemented by performing radiometric calibration, atmospheric correction, and cropping the target area on the raw remote sensing image data to obtain processed data; and removing typical land feature types from the processed data to obtain target remote sensing image data. Typical land feature types include one or more of forest, water, snow, and bare soil.

[0070] In one possible implementation, in order to achieve the removal of forests, water bodies, snow and bare soil, the typical land object types are removed from the processed data to obtain the target remote sensing image data. This step can be achieved through the following process: typical land object types are identified on the processed data based on the normalized vegetation index, the improved water body index and the normalized difference bare soil index to obtain non-greenhouse identification results; non-greenhouse area data corresponding to the non-greenhouse identification results are eliminated from the processed data to obtain target remote sensing image data.

[0071] The above-mentioned normalized difference vegetation index (NDVI) can be expressed as: NDVI = (B5-B4) / (B5+B4), the modified water body index (MNDWI) can be expressed as: MNDWI = (B3-B6) / (B3+B6), and the normalized difference bare soil index (NDBSI) can be expressed as: NDBSI = (B6-B2) / (B6+B2), where B2, B3, B4, B5 and B6 are the reflectances corresponding to the blue band, green band, red band, near infrared band and shortwave infrared 1 (SWIR1), respectively.

[0072] Forests, water bodies, snow, and bare soil can be identified using the Normalized Difference Vegetation Index, the Modified Water Index, and the Normalized Difference Bare Soil Index. Non-greenhouse identification results can include pixels belonging to typical ground feature types identified. The reflectance data corresponding to these pixels constitutes the non-greenhouse area data. By removing non-greenhouse area data from the processed data and performing greenhouse identification only on the remaining pixels in the target area, the influence of forests, water bodies, snow, and bare soil on greenhouse identification can be eliminated.

[0073] It should be noted that the specific process of performing radiometric calibration, atmospheric correction, and cropping of the target area on the original remote sensing image data can refer to the relevant existing technologies and will not be repeated here.

[0074] Step S130 , performing greenhouse recognition on the target remote sensing image data based on a preset MPGI to obtain a target greenhouse recognition result of the target area.

[0075] Among them, MPGI is defined as follows:

[0076]

[0077] v=(R Green -R Blue )×(R NIR -R SWIR1 -0.02);

[0078] Among them, R coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of short-wave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, and v is the preset initial discrimination parameter.

[0079] In some possible embodiments, the v value is first used for preliminary separation and judgment to strengthen the identification of greenhouses and suppress non-greenhouse type information. The MPGI value is then used for final greenhouse identification to improve the accuracy and recognition effect of the recognition result. Based on this, the above step S130 can be implemented through the following sub-steps 131 to 133:

[0080] Sub-step 131 traverses each pixel in the target remote sensing image data.

[0081] The pixels in the target remote sensing image data are the pixels remaining after removing the pixels belonging to the typical land feature type from the pixels corresponding to the target area.

[0082] Sub-step 132, for the current pixel traversed, based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, calculate the MPGI data corresponding to the current pixel, and determine the greenhouse identification result of the current pixel based on the MPGI data; wherein the MPGI data includes the initial discrimination parameter value (i.e., the v value) and the MPGI value.

[0083] In specific implementation, the initial discrimination parameter value corresponding to the current pixel can be calculated based on the first reflectivity data corresponding to the current pixel in the target remote sensing image data; wherein the first reflectivity data includes the reflectivity value of the blue band, the reflectivity value of the green band, the reflectivity value of the near infrared band and the reflectivity value of the short-wave infrared 1; it is judged whether the initial discrimination parameter value corresponding to the current pixel is greater than 0 to obtain a first judgment result; when the first judgment result is no, it is determined that the current pixel belongs to a non-greenhouse area; when the first judgment result is yes, based on the current pixel in the target remote sensing image data The second reflectivity data corresponding to the pixel is used to calculate the MPGI value corresponding to the current pixel; wherein the second reflectivity data includes the reflectivity value of the coastal aerosol band, the reflectivity value of the blue band, the reflectivity value of the near-infrared band, the reflectivity value of the shortwave infrared 1 and the reflectivity value of the shortwave infrared 2; it is judged whether the MPGI value corresponding to the current pixel is greater than or equal to the preset MPGI threshold to obtain a second judgment result; when the second judgment result is yes, it is determined that the current pixel belongs to the greenhouse area; when the second judgment result is no, it is determined that the current pixel belongs to the non-greenhouse area.

[0084] The MPGI threshold is a preset empirical value, which can be set according to experimental results and is not limited in the embodiment of the present invention.

[0085] Sub-step 133 is to traverse all pixels in the target remote sensing image data to obtain the target greenhouse recognition result in the target area.

[0086] The target greenhouse recognition result in the target area may include all pixels corresponding to the greenhouse area and all pixels corresponding to the non-greenhouse area in the target area.

[0087] Furthermore, to facilitate viewing of the target greenhouse identification results by the user, the greenhouse identification method further includes generating a greenhouse distribution map of the target area based on the target greenhouse identification results. When generating the greenhouse distribution map, all pixels corresponding to greenhouse areas and all pixels corresponding to non-greenhouse areas in the target area can be filled with different colors or grayscales, so that the user can intuitively see the distribution of greenhouses in the target area.

[0088] Furthermore, the greenhouse identification method further includes calculating a greenhouse ratio in the target area based on the target greenhouse identification result. When calculating the greenhouse ratio, a first number of all pixels corresponding to the greenhouse area in the target area and a second number of all pixels corresponding to the non-greenhouse area can be counted, and the greenhouse ratio is calculated using the following formula: Greenhouse ratio = first number / (first number + second number). This makes it easier for users to view the greenhouse ratio in the target area.

[0089] The greenhouse identification method provided by an embodiment of the present invention first obtains raw remote sensing image data of a target area, including reflectance data of preset bands, including the coastal aerosol band, the blue band, the green band, the near-infrared band, the shortwave infrared 1, and the shortwave infrared 2. The raw remote sensing image data is then preprocessed to obtain target remote sensing image data. Furthermore, greenhouse identification is performed on the target remote sensing image data based on a preset modified greenhouse index (MPGI), resulting in a target greenhouse identification result for the target area. Using the modified greenhouse index (MPGI) to identify greenhouses in the preprocessed target remote sensing image data takes into account the differences between greenhouses and non-greenhouses in the preset bands, and uses initial discriminant parameters to enhance greenhouse identification and suppress non-greenhouse information, thereby improving greenhouse identification accuracy and effectiveness.

[0090] For ease of understanding, the above-mentioned greenhouse identification method is introduced in detail below.

[0091] This embodiment of the present invention proposes an improved Plastic Greenhouse Index (MPGI) for identifying greenhouses in fragmented areas based on Landsat 8 OLI imagery. By using land cover type spectrum curves (i.e., the aforementioned feature type spectrum), separability analysis, and accuracy evaluation, it effectively enhances greenhouse information in fragmented areas.

[0092] See also Figure 2 The flowchart of another greenhouse identification method is shown in FIG. 1 , which uses the remote sensing image collected by Landsat 8 OLI ( Figure 2 The leftmost side shows the true color images of two regions, with color bands B4, B3 and B2) for spectral analysis to obtain the ground feature type spectrum (i.e. Figure 2 The spectrum of the land feature type includes the reflectance curves of 11 common land feature types from B1 to B7. The land feature type spectrum can be divided into the first spectrum (i.e. Figure 2 The spectrum at the top of the graph) and the second spectrum (i.e. Figure 2The first spectrum includes reflectance curves for four typical feature types at B1 to B7, and the second spectrum includes reflectance curves for seven remaining feature types at B1 to B7. By analyzing the first spectrum, NDVI (normalized difference vegetation index), mNDWI (modified water body index), and NDBSI (normalized difference bare soil index) can be used to perform typical feature analysis for the four typical feature types; the four typical feature types include forest, water, snow, and rare soil. By analyzing the second spectrum, MPGI can be used to perform residual feature analysis for the seven remaining feature types: artificial buildings (AS), blue buildings (BBuilding), mixed vegetation types (MVAS), plastic greenhouses (PG), reflective buildings (RAS), reflective blue buildings (RBB), and roads (RSoil). MPGI can be expressed as follows:

[0093]

[0094] r=R Green -R Blue

[0095] p=R NIR -R SWIR1 -0.02

[0096] v=r×p.

[0097] Combining the recognition results of the first spectrum and the second spectrum, we can get Figure 2 The recognition results corresponding to the MPGI in the rightmost image are (a1) corresponding to the true color image (f1) and (a2) corresponding to the true color image (f2); Figure 2 The rightmost side also shows the use of APGI, PGI, V I , PMLI and other greenhouse index recognition results, namely (b1), (c1), (d1), (e1) corresponding to the true color image (f1), and (b2), (c2), (d2), (e2) corresponding to the true color image (f2). Among them, the Advanced Plastic Greenhouse Index (APGI), the Plastic Greenhouse Index (PGI), the Vegetable Index (V I ) and the Plastic Mulch Film Index (PMLI) are both designed to identify plastic greenhouses. Figure 2 The vegetation in the identification results shown refers to forest.

[0098] The following is a detailed introduction to the MPGI proposal process.

[0099] Based on Landsat remote sensing imagery, spectral analysis was performed on common landforms to identify differences between greenhouse and non-greenhouse types across different spectral bands. Preprocessing and separation were used to enhance greenhouse information and suppress non-greenhouse types. Furthermore, a new greenhouse spectral index (MPGI) was proposed based on differences in B1, B2, and B5, B6, and B7. Visual interpretation and accuracy evaluation revealed that the new MPGI performed better and more accurately than the previous greenhouse index in fragmented areas.

[0100] 1. Preliminary preparation:

[0101] The OLI images of Landsat 8 with a long time series are used as research data, and the OLI images of Landsat 8 can be downloaded from relevant cloud platforms. There are more clouds in summer, and most crops are harvested in summer. Therefore, winter images (January 18, 2020 and March 30, 2020) are selected, with a cloud cover percentage of less than 0.3%. These images have 7 spectral bands, from the coastal aerosol band to SWIR2, with a spatial resolution of 30 meters. The influence of clouds was eliminated by radiometric calibration and atmospheric correction in ENVI (Environmental Visualization System) software, and the true surface spectral reflectance was obtained. Combined with Google Earth high-definition images, 7 common land feature types in fragmented areas (excluding the 4 typical land feature types of forests, water bodies, snow and bare soil) were selected to analyze the reflectance of each type in different bands, such as Figure 3 shown.

[0102] 2. Spectral analysis:

[0103] The revised Plastic Greenhouse Index (MPGI) will enhance information on various types of plastic greenhouses. Figure 3 The reflectance spectrum curves of PG, AS, RSoil, BBuilding, RBB, MVAS and RAS are shown in Figure 2. Figure 3 It can be seen that PG, RBB and MVAS have the highest reflectivity in B5, and the other types have higher reflectivity in B6. Similarly, the reflectivity of PG drops sharply from B5 to B7, while the other types of features increase or decrease slowly. Therefore, increasing the difference between B5 and B7 can separate greenhouses from non-greenhouses, and the spectral difference characteristics of greenhouses can be captured by the normalized difference index (corresponding to the difference between B5 and B7). In addition, other bands are selected to enhance the information in greenhouses. Figure 3As shown in the data, in the B1 and B2 bands, the reflectivity of PG is higher than that of AS and MVAS, but lower than that of RSoil, BBuilding, RBB and RAS. Therefore, these two bands are reliable indicators for distinguishing greenhouses from non-greenhouses.

[0104] However, the complex land cover types (i.e., land feature types) and the diversity of plastic greenhouses will cause the spectral characteristics of some land covers to be similar to those of greenhouses. In the combination of NIR-SWIR1 and NIR-SWIR2 bands, the separation between PG-RBB and PG-MVAS is poor. Therefore, this embodiment adopts separation judgment. Figure 3 As can be seen, the reflectance variations of RBB and PG are almost identical across all bands, but RBB's reflectance decreases between B2 and B3. This difference is represented by r. The r values for RBB and BBuilding are negative, while the r values for other feature types are positive. Furthermore, the MVAS, PG, RBB, and MVAS exhibit different decreasing trends between B5 and B6. The PG reflectance curve decreases significantly, while the RAS curve is flat. This difference in magnitude is represented by p. Here, we use the RBB reflectance variation as the standard, ensuring that the RBB's p value is positive. The p values for PG, RBB, and MVAS are positive, while the p values for other feature types are negative. Based on the above analysis, the product of r and p is positive for PG, BBuilding, and MVAS (for greenhouses and MVAS, both r and p are positive; for BBuilding, both r and p are negative). For the other feature types, r and p are positive, while the other features are negative (negative r and positive p for RBB; positive r and negative p for other feature types). Let v represent the product of r and p. When v is greater than 0, we propose to use MPGI to further separate greenhouses; otherwise (i.e., v is not greater than 0), it is classified as non-greenhouse. The final form of MPGI is:

[0105] r=R Green -R Blue

[0106] p=R NIR -R SWIR1 -0.02

[0107] v=r×p.

[0108] 3. Index applicable test:

[0109] In order to evaluate the applicability of the index, six greenhouse indices (MPGI, APGI, PGI, PMLI, V IIt is worth noting that when using MGPI, APGI, PGI, and VI, the values greater than their thresholds are classified as PG, while PMLI is the opposite (i.e., the values less than its threshold are classified as PG). Figure 4 The true color image (a) and the local magnified image (b) of a certain area show the greenhouse recognition results. The color bands are B4, B3 and B2, among which (a1) to (a6) are MPGI, APGI, PGI, PMLI, V I The recognition results of the true color image (a) corresponding to PGHI, (b1) to (b6) are MPGI, APGI, PGI, PMLI, V I The recognition results for the corresponding zoomed-in image (b) of the PGHI are shown. Visually, the MPGI recognition results are consistent with the real image, and MPGI can accurately extract the diversity and density of greenhouses. The Plastic Greenhouse Index (PGHI) can be expressed as B2 / B7. Values above this threshold are classified as greenhouses, where B2 and B7 are the reflectances corresponding to the blue band and shortwave infrared 2 (SWIR2), respectively.

[0110] MPGI, APGI, PGI, PMLI and V I The F1 values at different thresholds are as follows Figure 5 As shown in the figure. First, the overall accuracy trends of all greenhouse indices are largely similar, with a single peak and a gradual decline on both sides. Second, among the five greenhouse indices, MPGI has the highest mapping accuracy, with an F1 score of 88.6%. APGI is also effective in identifying greenhouses, with an F1 score of 86.7%. PGI and PMLI achieve an accuracy of 70%-80%. VI has an accuracy of less than 40%. It should be noted that the optimal thresholds for each greenhouse indices vary depending on the formula and dataset corresponding to the greenhouse index.

[0111] The embodiments of the present invention have the following beneficial effects:

[0112] 1. The separation and judgment step effectively suppresses non-greenhouse type information and enhances greenhouse identification.

[0113] 2. The MPGI index has strong adaptability to time and images, and has higher extraction accuracy in fragmented areas than the existing greenhouse index.

[0114] Corresponding to the above-mentioned greenhouse identification method, the embodiment of the present invention also provides a greenhouse identification device. Figure 6 The schematic diagram of the structure of a greenhouse identification device shown in FIG. 1 includes:

[0115] The data acquisition module 601 is used to acquire original remote sensing image data of the target area, wherein the original remote sensing image data includes reflectance data of preset bands, and the preset bands include coastal aerosol band, blue band, green band, near infrared band, shortwave infrared 1 and shortwave infrared 2;

[0116] A preprocessing module 602 is used to preprocess the original remote sensing image data to obtain target remote sensing image data;

[0117] The greenhouse identification module 603 is configured to perform greenhouse identification on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result for the target area. The MPGI is defined as follows:

[0118]

[0119] v=(R Green -R Blue )×(R NIR -R SWIR1 -0.02);

[0120] Among them, R coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of short-wave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, and v is the preset initial discrimination parameter.

[0121] The greenhouse identification device provided by an embodiment of the present invention first obtains raw remote sensing image data of a target area when performing greenhouse identification. The raw remote sensing image data includes reflectance data of preset bands, including the coastal aerosol band, the blue band, the green band, the near-infrared band, the shortwave infrared 1, and the shortwave infrared 2. The raw remote sensing image data is then preprocessed to obtain target remote sensing image data. Then, greenhouse identification is performed on the target remote sensing image data based on a preset modified greenhouse index (MPGI), obtaining a target greenhouse identification result for the target area. Using the modified greenhouse index (MPGI) to perform greenhouse identification on the preprocessed target remote sensing image data takes into account the differences between greenhouses and non-greenhouses in the preset bands, and uses initial discriminant parameters to enhance greenhouse identification and suppress non-greenhouse type information, thereby improving greenhouse identification accuracy and effectiveness.

[0122] Furthermore, the above-mentioned preprocessing module 602 is specifically used to: perform radiation calibration, atmospheric correction and cropping of the target area on the original remote sensing image data to obtain processed data; remove typical land object types from the processed data to obtain target remote sensing image data; wherein the typical land object types include one or more of forests, water bodies, snow and bare soil.

[0123] Furthermore, the above-mentioned preprocessing module 602 is also used to: identify the typical land feature type on the processed data based on the normalized vegetation index, the improved water body index and the normalized difference bare soil index to obtain a non-greenhouse identification result; and remove the non-greenhouse area data corresponding to the non-greenhouse identification result from the processed data to obtain the target remote sensing image data.

[0124] Furthermore, the greenhouse identification module 603 is specifically used to: traverse each pixel in the target remote sensing image data; for the current pixel traversed, based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, calculate the MPGI data corresponding to the current pixel, and determine the greenhouse identification result of the current pixel based on the MPGI data; wherein the MPGI data includes an initial discrimination parameter value and an MPGI value; until all pixels in the target remote sensing image data are traversed, the target greenhouse identification result of the target area is obtained.

[0125] Furthermore, the greenhouse recognition module 603 is also used to: calculate the initial discrimination parameter value corresponding to the current pixel based on the first reflectivity data corresponding to the current pixel in the target remote sensing image data; wherein the first reflectivity data includes the reflectivity value of the blue band, the reflectivity value of the green band, the reflectivity value of the near infrared band and the reflectivity value of the short-wave infrared 1; judge whether the initial discrimination parameter value corresponding to the current pixel is greater than 0, and obtain a first judgment result; when the first judgment result is no, determine that the current pixel belongs to a non-greenhouse area; when the first judgment result is yes, based on the target remote sensing image data, The second reflectivity data corresponding to the current pixel in the image data is used to calculate the MPGI value corresponding to the current pixel; wherein the second reflectivity data includes the reflectivity value of the coastal aerosol band, the reflectivity value of the blue band, the reflectivity value of the near-infrared band, the reflectivity value of the shortwave infrared 1 and the reflectivity value of the shortwave infrared 2; it is determined whether the MPGI value corresponding to the current pixel is greater than or equal to a preset MPGI threshold to obtain a second judgment result; when the second judgment result is yes, it is determined that the current pixel belongs to the greenhouse area; when the second judgment result is no, it is determined that the current pixel belongs to the non-greenhouse area.

[0126] Furthermore, the above device also includes:

[0127] A distribution map generating module is used to generate a greenhouse distribution map of the target area based on the target greenhouse identification result.

[0128] Furthermore, the above device also includes:

[0129] The proportion calculation module is used to calculate the greenhouse proportion value of the target area based on the target greenhouse identification result.

[0130] The device provided in this embodiment has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0131] like Figure 7 As shown, an electronic device 700 provided by an embodiment of the present invention includes: a processor 701, a memory 702 and a bus. The memory 702 stores a computer program that can be run on the processor 701. When the electronic device 700 is running, the processor 701 and the memory 702 communicate through the bus, and the processor 701 executes the computer program to implement the above-mentioned greenhouse identification method.

[0132] Specifically, the memory 702 and processor 701 can be general-purpose memories and processors, which are not specifically limited here.

[0133] The present invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program executes the greenhouse identification method described in the preceding method embodiments. The computer-readable storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), RAM, a magnetic disk, or an optical disk.

[0134] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0135] In all examples shown and described herein, any specific values should be interpreted as merely exemplary and not limiting, and thus other examples of the exemplary embodiments may have different values.

[0136] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A greenhouse identification method, characterized in that: include: Acquire original remote sensing image data of the target area, wherein the original remote sensing image data includes reflectance data of preset bands, wherein the preset bands include a coastal aerosol band, a blue band, a green band, a near infrared band, a shortwave infrared 1, and a shortwave infrared 2; Preprocessing the original remote sensing image data to obtain target remote sensing image data; Greenhouse identification is performed on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result for the target area; wherein the MPGI is defined as follows: ； v=（ R Green - R Blue ）×（ R NIR - R SWIR1 -0.02）； in, R Coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of shortwave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, v is the preset initial discrimination parameter; The greenhouse identification is performed on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain the target greenhouse identification result of the target area, including: Traversing each pixel in the target remote sensing image data; For the traversed current pixel, based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, the MPGI data corresponding to the current pixel is calculated, and the greenhouse identification result of the current pixel is determined based on the MPGI data; wherein the MPGI data includes an initial discrimination parameter value and an MPGI value; until all pixels in the target remote sensing image data are traversed to obtain the target greenhouse recognition result in the target area; The calculating of the MPGI data corresponding to the current pixel based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, and determining the greenhouse recognition result of the current pixel based on the MPGI data, includes: Based on the first reflectivity data corresponding to the current pixel in the target remote sensing image data, an initial discrimination parameter value corresponding to the current pixel is calculated; wherein the first reflectivity data includes a reflectivity value of a blue band, a reflectivity value of a green band, a reflectivity value of a near-infrared band, and a reflectivity value of a short-wave infrared 1; Determine whether the initial discrimination parameter value corresponding to the current pixel is greater than 0, and obtain a first judgment result; When the first judgment result is no, determining that the current pixel belongs to a non-greenhouse area; When the first judgment result is yes, the MPGI value corresponding to the current pixel is calculated based on the second reflectivity data corresponding to the current pixel in the target remote sensing image data; wherein the second reflectivity data includes the reflectivity value of the coastal aerosol band, the reflectivity value of the blue band, the reflectivity value of the near-infrared band, the reflectivity value of the shortwave infrared 1, and the reflectivity value of the shortwave infrared 2; Determine whether the MPGI value corresponding to the current pixel is greater than or equal to a preset MPGI threshold, and obtain a second determination result; When the second judgment result is yes, determining that the current pixel belongs to the greenhouse area; When the second judgment result is no, it is determined that the current pixel belongs to a non-greenhouse area.

2. The greenhouse identification method according to claim 1, characterized in that: The preprocessing of the original remote sensing image data to obtain target remote sensing image data includes: Performing radiometric calibration, atmospheric correction, and cropping of the target area on the original remote sensing image data to obtain processed data; Typical land object types are removed from the processed data to obtain target remote sensing image data; wherein the typical land object types include one or more of forests, water bodies, snow and bare soil.

3. The greenhouse identification method according to claim 2, characterized in that: Removing typical ground object types from the processed data to obtain target remote sensing image data includes: Identifying the typical land feature type on the processed data based on the normalized vegetation index, the improved water body index, and the normalized difference bare soil index to obtain a non-greenhouse identification result; The non-greenhouse area data corresponding to the non-greenhouse identification result is eliminated from the processed data to obtain target remote sensing image data.

4. The greenhouse identification method according to any one of claims 1 to 3, characterized in that: After greenhouse identification is performed on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain a target greenhouse identification result of the target area, the greenhouse identification method further includes: Based on the target greenhouse identification result, a greenhouse distribution map of the target area is generated.

5. The greenhouse identification method according to any one of claims 1 to 3, characterized in that: After greenhouse identification is performed on the target remote sensing image data based on the preset modified greenhouse index MPGI to obtain a target greenhouse identification result of the target area, the greenhouse identification method further includes: Based on the target greenhouse identification result, the greenhouse ratio value of the target area is calculated.

6. A greenhouse identification device, characterized in that: include: A data acquisition module is used to acquire original remote sensing image data of a target area, wherein the original remote sensing image data includes reflectance data of preset bands, wherein the preset bands include a coastal aerosol band, a blue band, a green band, a near infrared band, a shortwave infrared 1, and a shortwave infrared 2; A preprocessing module, configured to preprocess the original remote sensing image data to obtain target remote sensing image data; The greenhouse identification module is used to perform greenhouse identification on the target remote sensing image data based on a preset modified greenhouse index (MPGI) to obtain a target greenhouse identification result of the target area; wherein the MPGI is defined as follows: ； v=（ R Green - R Blue ）×（ R NIR - R SWIR1 -0.02）； in, R Coastal is the reflectivity of the coastal aerosol band, R Blue is the reflectivity of the blue band, R Green is the reflectance of the green band, R NIR is the reflectivity in the near-infrared band, R SWIR1 is the reflectivity of shortwave infrared 1, R SWIR2 is the reflectivity of shortwave infrared 2, v is the preset initial discrimination parameter; The greenhouse identification module is specifically configured to: traverse each pixel in the target remote sensing image data; for the current pixel traversed, calculate the MPGI data corresponding to the current pixel based on the reflectivity data corresponding to the current pixel in the target remote sensing image data, and determine the greenhouse identification result of the current pixel based on the MPGI data; wherein the MPGI data includes an initial discrimination parameter value and an MPGI value; until all pixels in the target remote sensing image data are traversed, a target greenhouse identification result of the target area is obtained; The greenhouse recognition module is also used to: calculate the initial discrimination parameter value corresponding to the current pixel based on the first reflectivity data corresponding to the current pixel in the target remote sensing image data; wherein the first reflectivity data includes the reflectivity value of the blue band, the reflectivity value of the green band, the reflectivity value of the near infrared band and the reflectivity value of the short-wave infrared 1; judge whether the initial discrimination parameter value corresponding to the current pixel is greater than 0, and obtain a first judgment result; when the first judgment result is no, determine that the current pixel belongs to a non-greenhouse area; when the first judgment result is yes, based on the target remote sensing image data The second reflectivity data corresponding to the current pixel in the image is used to calculate the MPGI value corresponding to the current pixel; wherein the second reflectivity data includes the reflectivity value of the coastal aerosol band, the reflectivity value of the blue band, the reflectivity value of the near-infrared band, the reflectivity value of the shortwave infrared 1 and the reflectivity value of the shortwave infrared 2; it is determined whether the MPGI value corresponding to the current pixel is greater than or equal to a preset MPGI threshold value to obtain a second judgment result; when the second judgment result is yes, it is determined that the current pixel belongs to the greenhouse area; when the second judgment result is no, it is determined that the current pixel belongs to the non-greenhouse area.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the greenhouse identification method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the greenhouse identification method according to any one of claims 1 to 5 is executed.

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