Ecological environment remote sensing image classification and feature extraction method based on deep learning

Abstract

The present invention proposes a method for classification and feature extraction of ecological environment remote sensing images based on deep learning. By acquiring remote sensing image data of the target ecological area, regional index data of five ecological indicators are obtained; ecological sensitivity evaluation and fusion of the five ecological indicators are performed to divide multiple ecological sensitive sub-areas; according to the ecological sensitivity data of each ecological sensitive sub-area, the optimal feature extraction algorithm is selected in combination with the importance weight coefficient to perform feature extraction to finally obtain a feature data set. The present invention can analyze and process remote sensing image data purposefully and with emphasis, and improves processing efficiency while meeting quality requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological product value realization, and in particular relates to an ecological environment remote sensing image classification and feature extraction method based on deep learning. Background Art

[0002] With growing awareness of ecological and environmental protection, the need for accurate classification and feature extraction of remote sensing imagery has become increasingly urgent. In recent years, the rapid development of deep learning technology has brought new breakthroughs in remote sensing image processing. Deep learning models, with their powerful automatic feature learning capabilities, can deeply explore the potential information in remote sensing imagery, providing strong support for accurate classification and feature extraction of ecological and environmental remote sensing imagery.

[0003] However, due to computing power limitations, some existing remote sensing image processing technologies cannot effectively analyze and process remote sensing images in a targeted and focused manner. This not only means that image data that meets quality requirements cannot be obtained, but also restricts the accuracy and efficiency of ecological environment monitoring and assessment. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an ecological environment remote sensing image classification and feature extraction method based on deep learning, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A method for ecological environment remote sensing image classification and feature extraction based on deep learning, the method specifically comprises the following steps:

[0007] Acquire remote sensing image data of the target ecological area, and perform data processing and index analysis on the remote sensing image data based on AI technology to obtain regional index data including five ecological indicators: elevation, slope, aspect, vegetation type, and land use type;

[0008] Based on the regional indicator data, an ecological sensitivity evaluation and integration of five ecological indicators, namely elevation, slope, aspect, vegetation type and land use type, is conducted to divide the target ecological region into multiple ecologically sensitive sub-regions;

[0009] Obtaining ecological sensitivity data corresponding to each of the ecologically sensitive sub-areas, wherein the ecological sensitivity data at least includes importance weight coefficients corresponding to elevation, slope, aspect, vegetation type, land use type, and texture features in the ecologically sensitive sub-areas;

[0010] When it is determined that the importance weight coefficient is greater than the preset importance weight coefficient, the optimal feature extraction algorithm model is selected in the current item for feature extraction to obtain the corresponding optimal sub-features, and the optimal sub-features corresponding to each item are integrated to generate a complete feature data set.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the acquiring of remote sensing image data of the target ecological area, performing data processing and index analysis on the remote sensing image data based on AI technology, and acquiring regional index data including five ecological indicators including elevation, slope, aspect, vegetation type, and land use type specifically include the following steps:

[0012] Identify target ecoregions;

[0013] Conducting remote sensing detection on the target ecological area to obtain remote sensing image data;

[0014] Based on AI technology, the remote sensing image data is subjected to radiometric calibration, atmospheric correction, geometric correction, cropping and splicing to obtain standard remote sensing data;

[0015] Index analysis is performed on the standard remote sensing data to obtain regional index data including five ecological indicators: elevation, slope, aspect, vegetation type and land use type.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the index analysis of the standard remote sensing data to obtain regional index data including five ecological indicators including elevation, slope, aspect, vegetation type and land use type specifically includes the following steps:

[0017] Using a digital elevation model, performing three-dimensional terrain analysis on the standard remote sensing data to obtain elevation data, slope data, and aspect data;

[0018] Performing spectral feature analysis and classification on the standard remote sensing data to obtain vegetation type data;

[0019] Performing color and texture analysis and classification on the standard remote sensing data to obtain land use type data;

[0020] The elevation data, the slope data, the aspect data, the vegetation type data and the land use type data are integrated to generate regional index data.

[0021] As a further limitation of the technical solution of the embodiment of the present invention, performing ecological sensitivity evaluation and integration of five ecological indicators of elevation, slope, aspect, vegetation type, and land use type based on the regional indicator data, and dividing the target ecological region into multiple ecologically sensitive sub-regions specifically includes the following steps:

[0022] Conducting elevation ecological sensitivity assessment based on the regional indicator data to obtain elevation assessment data;

[0023] Conducting an ecological sensitivity assessment of the slope based on the regional indicator data to obtain slope assessment data;

[0024] Conducting an ecological sensitivity assessment of the slope aspect based on the regional indicator data to obtain slope aspect assessment data;

[0025] Conducting an ecological sensitivity assessment of vegetation types based on the regional indicator data to obtain vegetation type assessment data;

[0026] Conducting ecological sensitivity assessment of land use types based on the regional indicator data to obtain land use type assessment data;

[0027] The elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data and the land use type evaluation data are integrated to divide the area into multiple ecologically sensitive sub-areas.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, the fusing of the elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data to divide the plurality of ecologically sensitive sub-areas specifically includes the following steps:

[0029] Determine the fusion evaluation weight;

[0030] According to the fusion evaluation weights, the elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data are fused to obtain fused evaluation data;

[0031] Analyzing the fused evaluation data to determine the sensitive level boundaries of the target ecological area;

[0032] According to the sensitivity level boundaries, the target ecological area is divided into multiple ecologically sensitive sub-areas.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] This method acquires remote sensing imagery data of a target ecological region and obtains regional indicator data for five ecological indicators. It then performs an ecological sensitivity assessment and integration of these five indicators, dividing the region into multiple ecologically sensitive subregions. Based on the ecological sensitivity data for each subregion and combining it with importance weight coefficients, it selects an optimal feature extraction algorithm to extract features, ultimately generating a feature dataset. This method enables targeted and focused analysis and processing of remote sensing imagery data, improving processing efficiency while still meeting quality requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0036] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0037] Figure 2 A flow chart of data processing and indicator analysis in the method provided by an embodiment of the present invention is shown.

[0038] Figure 3 A flow chart of obtaining regional indicator data in the method provided by an embodiment of the present invention is shown.

[0039] Figure 4 A flowchart of ecological sensitivity assessment and integration in the method provided by an embodiment of the present invention is shown.

[0040] Figure 5 A flowchart of dividing ecologically sensitive sub-areas in the method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] Due to computing power limitations, some existing remote sensing image processing technologies cannot effectively analyze and process remote sensing images in a targeted and focused manner. This not only prevents the acquisition of image data that meets quality requirements, but also restricts the accuracy and efficiency of ecological environment monitoring and assessment.

[0043] To address these issues, the present invention acquires remote sensing imagery data for a target ecological region and regional indicator data for five ecological indicators. Ecological sensitivity assessment and integration of these five indicators are then performed to divide the region into multiple ecologically sensitive subregions. Based on the ecological sensitivity data for each subregion, the optimal feature extraction algorithm is selected in combination with importance weight coefficients to ultimately generate a feature dataset. This invention enables targeted and focused analysis and processing of remote sensing imagery data, improving processing efficiency while still meeting quality requirements.

[0044] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0045] Specifically, a method for ecological environment remote sensing image classification and feature extraction based on deep learning, the method specifically includes the following steps:

[0046] Step S101: Acquire remote sensing image data of the target ecological area, perform data processing and index analysis on the remote sensing image data based on AI technology, and obtain regional index data including five ecological indicators: elevation, slope, aspect, vegetation type, and land use type.

[0047] In an embodiment of the present invention, by determining the target ecological area, remote sensing detection is performed on the target ecological area to obtain remote sensing image data, and then based on AI technology, radiometric calibration, atmospheric correction, geometric correction, cropping and splicing are performed on the remote sensing image data to eliminate the influence of factors such as atmosphere and terrain on reflectivity, and ensure the integrity and consistency of the data, generate standard remote sensing data, use digital elevation model and remote sensing image processing software to perform three-dimensional terrain analysis on the standard remote sensing data, and obtain elevation data, slope data and slope direction data, wherein the elevation data reflects the terrain undulation, slope, and slope direction data. The data reflects the steepness of the surface, and the slope data indicates the direction of the surface. The spectral feature analysis and classification of standard remote sensing data are performed to extract different types of vegetation such as forests, grasslands, and shrubs to obtain vegetation type data. The color and texture feature analysis and classification of the standard remote sensing data are then performed. Combined with machine learning or deep learning algorithms, land use type data including cultivated land, forest land, construction land, and water areas are obtained. Then, the elevation data, slope data, slope aspect data, vegetation type data, and land use type data are integrated to generate regional indicator data for the target ecological area.

[0048] Specifically, Figure 2 A flow chart of data processing and indicator analysis in the method provided by an embodiment of the present invention is shown.

[0049] In a preferred embodiment of the present invention, obtaining remote sensing image data of a target ecological area, performing data processing and index analysis on the remote sensing image data based on AI technology, and obtaining regional index data including five ecological indicators, including elevation, slope, aspect, vegetation type, and land use type, specifically comprises the following steps:

[0050] Step S1011, determining the target ecological area;

[0051] Step S1012: performing remote sensing detection on the target ecological area to obtain remote sensing image data;

[0052] Step S1013: Based on AI technology, radiometric calibration, atmospheric correction, geometric correction, cropping, and splicing are performed on the remote sensing image data to obtain standard remote sensing data.

[0053] Step S1014: performing index analysis on the standard remote sensing data to obtain regional index data including five ecological indicators: elevation, slope, aspect, vegetation type, and land use type.

[0054] Specifically, based on AI technology, the process of performing radiometric calibration on the remote sensing image data includes the following steps:

[0055] Obtain pixel values ​​(DN values) in remote sensing image data, use the sensor's gain and offset parameters to perform initial linear calibration on the pixel values, and obtain preliminary radiation values;

[0056] According to the solar irradiance and solar zenith angle, the preliminary radiation value is normalized so that the processed radiation value can adapt to different observation conditions and obtain the normalized radiation value;

[0057] Correcting the normalized radiation value according to the spectral response of the sensor to adjust the radiation value to improve accuracy, thereby obtaining a first corrected radiation value;

[0058] Normalize the pixel values ​​and input them into the deep learning model for prediction to obtain the second corrected radiation value;

[0059] Combining the first corrected radiation value and the second corrected radiation value, and setting a mixing factor to adjust the importance of the first corrected radiation value and the second corrected radiation value to obtain a combined corrected radiation value;

[0060] An optimization function is used to determine the optimal mixing factor to minimize the error between the combined corrected radiation value and the second corrected radiation value to obtain the optimal mixing factor, and the optimal mixing factor is used to adjust the importance of the first corrected radiation value and the second corrected radiation value to obtain the final corrected radiation value.

[0061] Specifically, based on AI technology, the process of performing atmospheric correction on the remote sensing image data includes the following steps:

[0062] Construct an atmospheric radiation transfer model, which divides the atmosphere into multiple layers, each with different aerosol concentrations and water vapor contents, to more accurately simulate the impact of the atmosphere on radiation. The model also provides initial parameters for aerosol concentrations and water vapor contents.

[0063] Using the final corrected radiation value as input, calculate the absorption and scattering effects of each atmospheric layer on radiation to obtain the attenuation effect of each layer, and superimpose the total radiation attenuation to obtain the surface reflectivity;

[0064] After the optimization of the initial parameters of the model is completed using genetic algorithm, the final surface reflectivity is obtained;

[0065] Based on a deep neural network (DNN), the final surface reflectivity is used as training data to learn and fit a complex atmospheric radiation transfer model to improve the calculation speed and accuracy and obtain the corrected surface reflectivity.

[0066] Specifically, based on AI technology, the process of geometrically correcting the remote sensing image data includes the following steps:

[0067] The observation points of remote sensing images are converted into geographic coordinates using projection transformation matrix and normalized;

[0068] Calculate the difference between the converted geographic coordinates and the actual geographic coordinates;

[0069] According to the difference between the converted geographic coordinates and the actual geographic coordinates, the unit matrix is ​​used as the initial estimate, and the projection transformation matrix is ​​optimized by the least square method to minimize the difference and obtain the optimized projection transformation matrix;

[0070] Use the optimized projection transformation matrix to perform preliminary geometric correction on the remote sensing image to obtain a preliminary geometrically corrected image;

[0071] The image with preliminary geometric correction is used as input data and input into the convolutional neural network for prediction, so as to obtain the predicted geographic coordinates by performing nonlinear adjustment through the convolutional neural network;

[0072] The convolutional neural network is trained with the actual geographic coordinates corresponding to the geographic coordinates in the preliminary geometrically corrected image as the target data. After the training is completed, the final predicted geographic coordinates are obtained, and then the final predicted geographic coordinates are interpolated back to the image space to finally obtain the geometrically corrected image.

[0073] Specifically, Figure 3 A flow chart of obtaining regional indicator data in the method provided by an embodiment of the present invention is shown.

[0074] In a preferred embodiment of the present invention, the index analysis of the standard remote sensing data to obtain regional index data including five ecological indicators, including elevation, slope, aspect, vegetation type, and land use type, specifically includes the following steps:

[0075] Step S10141, using a digital elevation model, performing a three-dimensional terrain analysis on the standard remote sensing data to obtain elevation data, slope data, and aspect data;

[0076] Step S10142, performing spectral feature analysis and classification on the standard remote sensing data to obtain vegetation type data;

[0077] Step S10143, performing color and texture analysis and classification on the standard remote sensing data to obtain land use type data;

[0078] Step S10144: integrating the elevation data, the slope data, the aspect data, the vegetation type data, and the land use type data to generate regional index data.

[0079] Specifically, using a digital elevation model to perform three-dimensional terrain analysis on the standard remote sensing data to obtain elevation data, slope data, and aspect data specifically includes the following steps:

[0080] Step S10141a: Process the calibrated remote sensing data using a digital elevation model to obtain elevation information of each grid point, and obtain elevation data by performing a weighted summation on the elevation information of each grid point;

[0081] Step S10141b, by calculating the elevation data x Direction and y The derivative of the direction is used to obtain the slope data;

[0082] Step S10141c, by calculating the elevation data x Direction and y After taking the ratio of the derivative of the direction, the inverse tangent function is taken to obtain the aspect data.

[0083] Specifically, performing spectral feature analysis and classification on the standard remote sensing data to obtain vegetation type data specifically includes the following steps:

[0084] Step S10142a: performing normalization processing based on the reflectance value of the near-infrared band and the reflectance value of the red band to obtain a normalized vegetation index;

[0085] In this step, specifically, the reflection value of the red band is subtracted from the reflection value of the near infrared band to obtain a reflection difference, and then the reflection difference is divided by the sum of the reflection value of the near infrared band and the reflection value of the red band to obtain the normalized vegetation index.

[0086] Step S10142b: Subtract the reflection value of the near-infrared band from the reflection value of the red band to obtain a reflection difference, obtain a correction value of the red band and a correction value of the blue band, and calculate the enhanced vegetation index based on the reflection difference, the correction value of the red band, and the correction value of the blue band;

[0087] Specifically, in this step, the gain factor is multiplied by the reflectance difference to obtain a first numerator, which is then divided by a first denominator to obtain the enhanced vegetation index. The first denominator is the reflectance value of the near-infrared band plus the correction value of the red band minus the correction value of the blue band, plus the radiation transfer correction parameter.

[0088] Step S10142c: obtaining a first adjustment parameter, and calculating a land surface adjusted vegetation index based on the first adjustment parameter and the reflection difference;

[0089] Specifically, in this step, the reflectance difference * (1 + first adjustment parameter) is used as the second numerator, the sum of the reflectance value of the near-infrared band, the reflectance value of the red band, and the first adjustment parameter is used as the second denominator, and the second numerator is divided by the second denominator to obtain the land surface adjusted vegetation index;

[0090] Step S10142d: compose the normalized vegetation index, the enhanced vegetation index, and the land surface adjusted vegetation index into a feature vector, perform principal component analysis on the feature vector, perform dimensionality reduction on the data, and obtain a feature vector after dimensionality reduction;

[0091] Step S10142e: Use a support vector machine to classify the feature vector after dimensionality reduction to determine the vegetation type of each pixel.

[0092] Specifically, performing color texture analysis and classification on the standard remote sensing data to obtain land use type data specifically includes the following steps:

[0093] Step S10143a: For each pixel, calculate a color histogram based on its reflectance values ​​in different bands;

[0094] Step S10143b: For each pixel, calculate its color moment in a specific band. The color moment includes the color mean in the specific band, the standard deviation of the color in the specific band, and the skewness of the color in the specific band.

[0095] Step S10143c: For each pixel, calculate the gray level co-occurrence matrix in a specific band, and extract texture features from the gray level co-occurrence matrix. The entropy texture features include contrast, energy, homogeneity, and entropy.

[0096] Step S10143d: for each pixel, calculate its local binary pattern;

[0097] Step S10143e: The color histogram, color moment, texture feature, and local binary pattern are combined into a feature vector to obtain a comprehensive feature. A convolutional neural network (CNN) is used to perform feature fusion and classification on the comprehensive feature vector to obtain the land use type of each pixel.

[0098] Furthermore, the ecological environment remote sensing image classification and feature extraction method based on deep learning also includes the following steps:

[0099] Step S102 : Based on the regional index data, ecological sensitivity evaluation and integration of five ecological indicators, namely elevation, slope, aspect, vegetation type and land use type, are performed to divide the target ecological region into a plurality of ecologically sensitive sub-regions.

[0100] In an embodiment of the present invention, an ecological sensitivity evaluation of elevation is performed based on regional indicator data to obtain elevation evaluation data; an ecological sensitivity evaluation of slope is performed based on regional indicator data to obtain slope evaluation data; an ecological sensitivity evaluation of aspect is performed based on regional indicator data to obtain aspect evaluation data; an ecological sensitivity evaluation of vegetation type is performed based on regional indicator data to obtain vegetation type evaluation data; and an ecological sensitivity evaluation of land use type is performed based on regional indicator data to obtain land use type evaluation data; then a fusion evaluation weight is determined; and then, according to the fusion evaluation weight, the elevation evaluation data, slope evaluation data, aspect evaluation data, vegetation type evaluation data and land use type evaluation data are fused to obtain fusion evaluation data; by analyzing the fusion evaluation data, the sensitivity level boundaries of different sensitivity levels in the target ecological area are determined; and according to the sensitivity level boundaries, the target ecological area is divided into multiple ecological sensitive sub-areas of different sensitivity levels, including extremely sensitive sub-areas, highly sensitive sub-areas, moderately sensitive sub-areas, slightly sensitive sub-areas and insensitive sub-areas.

[0101] Specifically, Figure 4 A flowchart of ecological sensitivity assessment and integration in the method provided by an embodiment of the present invention is shown.

[0102] In a preferred embodiment of the present invention, the ecological sensitivity evaluation and integration of five ecological indicators, namely elevation, slope, aspect, vegetation type, and land use type, are performed based on the regional indicator data, and the target ecological region is divided into a plurality of ecologically sensitive sub-regions, specifically comprising the following steps:

[0103] Step S1021: performing an ecological sensitivity assessment of elevation based on the regional index data to obtain elevation assessment data;

[0104] Step S1022: performing an ecological sensitivity assessment of the slope based on the regional index data to obtain slope assessment data;

[0105] Step S1023: performing an ecological sensitivity assessment of the slope aspect based on the regional index data to obtain slope aspect assessment data;

[0106] Step S1024: performing an ecological sensitivity assessment of vegetation types based on the regional index data to obtain vegetation type assessment data;

[0107] Step S1025: performing an ecological sensitivity assessment of land use types based on the regional index data to obtain land use type assessment data;

[0108] Step S1026 , integrating the elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data to divide the area into multiple ecologically sensitive sub-areas.

[0109] Specifically, Figure 5 A flowchart of dividing ecologically sensitive sub-areas in the method provided by an embodiment of the present invention is shown.

[0110] In a preferred embodiment of the present invention, the integration of the elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data to divide the plurality of ecologically sensitive sub-areas specifically includes the following steps:

[0111] Step S10261, determining the fusion evaluation weight;

[0112] Step S10262: fusing the elevation evaluation data, the slope evaluation data, the aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data according to the fusion evaluation weights to obtain fused evaluation data.

[0113] Step S10263: Analyze the fused evaluation data to determine the sensitivity level boundary of the target ecological area;

[0114] Step S10264: Divide the target ecological region into multiple ecologically sensitive sub-regions according to the sensitivity level boundaries.

[0115] Furthermore, the ecological environment remote sensing image classification and feature extraction method based on deep learning also includes the following steps:

[0116] Step S103: Obtain ecological sensitivity data corresponding to each ecologically sensitive sub-area, wherein the ecological sensitivity data at least includes importance weight coefficients corresponding to elevation, slope, aspect, vegetation type, land use type, and texture features in the ecologically sensitive sub-area.

[0117] In step S104, when it is determined that the importance weight coefficient is greater than the preset importance weight coefficient, the optimal feature extraction algorithm model is selected in the current item to perform feature extraction to obtain the corresponding optimal sub-features, and the optimal sub-features corresponding to each item are integrated to generate a complete feature data set.

[0118] Specifically, step S104 includes the following sub-steps:

[0119] Step S1041: When it is determined that the importance weight coefficient of the elevation item is greater than the preset importance weight coefficient, the average elevation characteristics and elevation range characteristics of each ecologically sensitive sub-area are extracted using a digital elevation model;

[0120] Step S1042: When it is determined that the importance weight coefficient of the slope item or the aspect item is greater than the preset importance weight coefficient, the slope and aspect of each ecologically sensitive sub-area are calculated using a spatial analysis algorithm based on a digital elevation model;

[0121] Step S1043: When it is determined that the importance weight coefficient of the vegetation type item or the land use type item is greater than the preset importance weight coefficient, the maximum likelihood classification algorithm is used to calculate and determine the vegetation type item or land use type corresponding to each ecologically sensitive sub-area;

[0122] In step S1044 , when it is determined that the importance weight coefficient of the texture feature item is greater than the preset importance weight coefficient, the texture features of each ecologically sensitive sub-region are extracted using the gray level co-occurrence matrix.

[0123] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0125] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for classification and feature extraction of ecological environment remote sensing images based on deep learning, characterized in that: The method specifically comprises the following steps: Acquire remote sensing image data of the target ecological area, and perform data processing and index analysis on the remote sensing image data based on AI technology to obtain regional index data including five ecological indicators: elevation, slope, aspect, vegetation type, and land use type; Based on the regional indicator data, ecological sensitivity evaluation and integration of five ecological indicators, namely elevation, slope, aspect, vegetation type and land use type, are carried out to divide the target ecological area into multiple ecologically sensitive sub-areas; Obtaining ecological sensitivity data corresponding to each of the ecologically sensitive sub-areas, wherein the ecological sensitivity data at least includes importance weight coefficients corresponding to elevation, slope, aspect, vegetation type, land use type, and texture features in the ecologically sensitive sub-areas; When it is determined that the importance weight coefficient is greater than the preset importance weight coefficient, the optimal feature extraction algorithm model is selected in the current item to perform feature extraction to obtain the corresponding optimal sub-features, and the optimal sub-features corresponding to each item are integrated to generate a complete feature data set; The step of acquiring remote sensing image data of the target ecological area, performing data processing and index analysis on the remote sensing image data based on AI technology, and acquiring regional index data including five ecological indexes including elevation, slope, aspect, vegetation type and land use type specifically includes the following steps: Identify target ecoregions; Conduct remote sensing detection on the target ecological area to obtain remote sensing image data; Based on AI technology, the remote sensing image data is subjected to radiation calibration, atmospheric correction, geometric correction, cropping and splicing to obtain standard remote sensing data; Performing index analysis on the standard remote sensing data to obtain regional index data including five ecological indexes: elevation, slope, aspect, vegetation type and land use type; Based on AI technology, the process of radiometric calibration of the remote sensing image data specifically includes the following steps: Obtain pixel values ​​in remote sensing image data, use the gain and offset parameters of the sensor to perform initial linear calibration on the pixel values, and obtain preliminary radiation values; According to the solar irradiance and the solar zenith angle, the preliminary radiation value is normalized to obtain the normalized radiation value; Correcting the normalized radiation value according to the spectral response of the sensor to obtain a first corrected radiation value; The pixel values ​​are normalized and input into the deep learning model for prediction to obtain the second corrected radiation value; The first corrected radiation value and the second corrected radiation value are combined, and a mixing factor is set to adjust the importance of the first corrected radiation value and the second corrected radiation value to obtain a combined corrected radiation value; Using an optimization function to determine an optimal mixing factor, minimizing the error between the combined corrected radiation value and the second corrected radiation value, obtaining the optimal mixing factor, and using the optimal mixing factor to adjust the importance of the first corrected radiation value and the second corrected radiation value, obtaining a final corrected radiation value; Based on AI technology, the process of performing atmospheric correction on the remote sensing image data specifically includes the following steps: Construct an atmospheric radiation transfer model. The atmospheric radiation transfer model divides the atmosphere into multiple layers, each layer has different aerosol concentrations and water vapor contents, and the initial model parameters of aerosol concentrations and water vapor contents are given; Using the final corrected radiation value as input, calculate the absorption and scattering effects of each atmospheric layer on radiation to obtain the attenuation effect of each layer, and superimpose the total radiation attenuation to obtain the surface reflectivity; After the optimization of the initial parameters of the model is completed using genetic algorithm, the final surface reflectivity is obtained; Based on a deep neural network, the final surface reflectivity is used as training data to learn and fit the complex atmospheric radiation transfer model to obtain the corrected surface reflectivity.

2. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 1, characterized in that: The step of performing index analysis on the standard remote sensing data to obtain regional index data including five ecological indexes, including elevation, slope, aspect, vegetation type and land use type, specifically comprises the following steps: Using a digital elevation model, a three-dimensional terrain analysis is performed on the standard remote sensing data to obtain elevation data, slope data and aspect data; Performing spectral feature analysis and classification on the standard remote sensing data to obtain vegetation type data; Performing color texture analysis and classification on the standard remote sensing data to obtain land use type data; The elevation data, the slope data, the aspect data, the vegetation type data and the land use type data are integrated to generate regional index data.

3. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 2 is characterized in that: Using a digital elevation model, performing a three-dimensional terrain analysis on the standard remote sensing data to obtain elevation data, slope data, and aspect data specifically includes the following steps: The calibrated remote sensing data is processed using a digital elevation model to obtain the elevation information of each grid point, and the elevation data is obtained by weighted summation of the elevation information of each grid point; By calculating the elevation data in x Direction and y The derivative of the direction gives the slope data; By calculating the elevation data in x Direction and y After taking the ratio of the derivatives of the directions, take the inverse tangent function to get the aspect data.

4. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 3 is characterized in that: Performing spectral feature analysis and classification on the standard remote sensing data to obtain vegetation type data specifically includes the following steps: Based on the reflectance value of the near infrared band and the reflectance value of the red band, normalization processing is performed to obtain the normalized vegetation index; The reflection value of the near-infrared band is subtracted from the reflection value of the red band to obtain the reflection difference, the correction value of the red band and the correction value of the blue band are obtained, and the enhanced vegetation index is calculated according to the reflection difference, the correction value of the red band and the correction value of the blue band; Obtaining a first adjustment parameter, and calculating a land surface adjusted vegetation index according to the first adjustment parameter and the reflection difference; The normalized vegetation index, enhanced vegetation index and land surface adjusted vegetation index are combined into a feature vector, and principal component analysis is performed on the feature vector to reduce the dimension of the data to obtain the feature vector after dimension reduction; The support vector machine is used to classify the feature vector after dimension reduction to determine the vegetation type of each pixel.

5. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 4, characterized in that: Performing color texture analysis and classification on the standard remote sensing data to obtain land use type data specifically includes the following steps: For each pixel, the color histogram is calculated based on the reflectance values ​​of different bands; For each pixel, calculate its color moment in a specific band. The color moment includes the color mean in a specific band, the standard deviation of the color in a specific band, and the skewness of the color in a specific band. For each pixel, the gray level co-occurrence matrix is ​​calculated in a specific band, and texture features are extracted from the gray level co-occurrence matrix. The entropy texture features include contrast, energy, homogeneity and entropy. For each pixel, calculate its local binary pattern; The color histogram, color moment, texture features and local binary patterns are combined into a feature vector to obtain a comprehensive feature. The convolutional neural network performs feature fusion and classification on the comprehensive feature vector to obtain the land use type of each pixel.

6. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 5, characterized in that: The method of performing ecological sensitivity evaluation and integration of five ecological indicators, namely, elevation, slope, aspect, vegetation type and land use type, based on the regional indicator data, and dividing the target ecological region into a plurality of ecologically sensitive sub-regions specifically includes the following steps: Conducting ecological sensitivity assessment of elevation based on the regional index data to obtain elevation assessment data; Conducting an ecological sensitivity assessment of the slope based on the regional index data to obtain slope assessment data; Conducting ecological sensitivity assessment of slope aspect according to the regional index data to obtain slope aspect assessment data; Conducting an ecological sensitivity assessment of vegetation types based on the regional indicator data to obtain vegetation type assessment data; Conducting ecological sensitivity assessment of land use types based on the regional indicator data to obtain land use type assessment data; The elevation evaluation data, the slope evaluation data, the slope aspect evaluation data, the vegetation type evaluation data and the land use type evaluation data are integrated to divide the area into multiple ecologically sensitive sub-areas.

7. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 6, characterized in that: The step of fusing the elevation evaluation data, the slope evaluation data, the slope aspect evaluation data, the vegetation type evaluation data, and the land use type evaluation data to divide a plurality of ecologically sensitive sub-areas specifically comprises the following steps: Determine the fusion evaluation weight; According to the fusion evaluation weight, the elevation evaluation data, the slope evaluation data, the slope aspect evaluation data, the vegetation type evaluation data and the land use type evaluation data are fused to obtain fusion evaluation data; Analyzing the fused evaluation data to determine the sensitive level boundary of the target ecological area; According to the sensitivity level boundaries, the target ecological area is divided into multiple ecologically sensitive sub-areas.

8. The method for classification and feature extraction of ecological environment remote sensing images based on deep learning according to claim 7, characterized in that: When it is determined that the importance weight coefficient is greater than the preset importance weight coefficient, the optimal feature extraction algorithm model is selected in the current item to perform feature extraction to obtain the corresponding optimal sub-feature, which specifically includes the following steps: When it is determined that the importance weight coefficient of the elevation item is greater than the preset importance weight coefficient, the digital elevation model is used to extract the average elevation characteristics and elevation range characteristics in each ecologically sensitive sub-area; When it is determined that the importance weight coefficient of the slope item or the aspect item is greater than the preset importance weight coefficient, the slope and aspect of each ecologically sensitive sub-area are calculated using a spatial analysis algorithm based on a digital elevation model; When it is determined that the importance weight coefficient of the vegetation type item or the land use type item is greater than the preset importance weight coefficient, the maximum likelihood classification algorithm is used to calculate and determine the vegetation type item or land use type corresponding to each ecologically sensitive sub-area; When it is determined that the importance weight coefficient of the texture feature item is greater than the preset importance weight coefficient, the gray level co-occurrence matrix is ​​used to extract the texture features of each ecologically sensitive sub-area.

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