A remote sensing hyperspectral image classification method based on digital elevation model feature extraction

Abstract

A remote sensing hyperspectral image classification method based on digital elevation model feature extraction belongs to the technical field of hyperspectral image classification. The present invention solves the problem of low classification accuracy of existing hyperspectral remote sensing image classification methods. The present invention obtains a digital elevation model (DEM), a digital elevation model (INSAR-DEM) and a Landsat8 satellite remote sensing hyperspectral image of the area to be classified. Based on the digital elevation model (DEM) and the digital elevation model (INSAR-DEM), a final corrected digital elevation model (DEM) can be obtained. The final digital elevation model (DEM) is then combined with the elevation features of the final digital elevation model (DEM) and the spectral features of the image for classification. Since the classification process of the present invention takes elevation information into account, the classification accuracy is effectively improved. The method of the present invention can be applied to remote sensing hyperspectral image classification.

Description

Technical Field

[0001] The present invention belongs to the technical field of hyperspectral image classification, and in particular relates to a remote sensing hyperspectral image classification method based on digital elevation model feature extraction. Background Art

[0002] Hyperspectral remote sensing is a new remote sensing technology characterized by the integration of remote sensing images and spectra. Its spectral resolution can reach as high as nanometers, and it has a wide range of applications in fields such as mineral exploration, precision agriculture, and environmental monitoring. Therefore, studying the efficient processing and interpretation of hyperspectral data has important theoretical significance and practical application value. Hyperspectral classification and target detection are the main contents of hyperspectral data processing. Hyperspectral data provides rich spectral information, creating conditions for people to study the characteristics of surface objects and perform ground object identification. Many classification and feature extraction methods have been developed. With the development of machine learning and deep learning technologies, hyperspectral image classification based on machine learning and deep learning has made breakthrough progress in methods and performance. For example, the nearest neighbor classifier and support vector machine classifier in machine learning, the autoencoder neural network in the deep learning model can achieve nonlinear dimensionality reduction of high-dimensional data, and the convolutional neural network (CNN) can automatically extract spatial features from images and then perform classification based on the extracted spatial features.

[0003] However, the existing hyperspectral remote sensing image classification methods only consider the image features themselves, and do not consider the elevation features of the image area. Therefore, the classification accuracy of the existing hyperspectral remote sensing image classification methods is still low. It is very necessary to propose a hyperspectral remote sensing image classification method that combines elevation features. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem of low classification accuracy of existing hyperspectral remote sensing image classification methods, and propose a remote sensing hyperspectral image classification method based on digital elevation model feature extraction.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a remote sensing hyperspectral image classification method based on digital elevation model feature extraction, the method specifically comprising the following steps:

[0006] Step 1: Obtain the digital elevation model (DEM), digital elevation model (INSAR-DEM) and Landsat8 satellite remote sensing hyperspectral image of the area to be classified;

[0007] Step 2: According to the digital elevation model (DEM) and the digital elevation model (INSAR-DEM), the abnormal pixel values in the digital elevation model (DEM) are determined, and the abnormal pixel values in the digital elevation model (DEM) are corrected according to the correction values of the abnormal pixels to obtain a corrected digital elevation model (DEM);

[0008] Step 3: Using the corrected digital elevation model DEM obtained in step 2 as input to the first neural network model, and outputting the final digital elevation model DEM through the first neural network model;

[0009] Step 4: Use the Landsat 8 satellite remote sensing hyperspectral image as the input of the second neural network model to extract the spectral features of the image;

[0010] Step 5: Extract elevation features from the final digital elevation model (DEM), and then combine the elevation features with the spectral features to obtain combined features.

[0011] The combined features are input into the support vector machine, and the classification results are output through the support vector machine.

[0012] Furthermore, the method of determining abnormal pixel values in the digital elevation model DEM based on the digital elevation model DEM and the digital elevation model INSAR-DEM is as follows:

[0013] Step 1: Obtain the corresponding pixel value of each pixel value in the digital elevation model DEM in the digital elevation model INSAR-DEM, and convert the pixel value a in the digital elevation model DEM into i The corresponding pixel value in the digital elevation model INSAR-DEM is recorded as b i , i = 1, 2, ..., n, n is the total number of pixel values in the digital elevation model DEM, that is, the total number of pixel values in the digital elevation model INSAR-DEM;

[0014] Step 2: Calculate a i and b i The average value c i , i=1,2,…,n;

[0015] Step 3: Calculate the deviation value |a i -c i |, |·| represent taking absolute values;

[0016] If the deviation value |a i -c i | is greater than the set threshold, then a i is the abnormal pixel value in the digital elevation model DEM;

[0017] If the deviation value |a i -c i| is less than or equal to the set threshold, then a i is the normal pixel value in the digital elevation model DEM;

[0018] Step 4: c i As the abnormal pixel value a i Corrected value.

[0019] Furthermore, the abnormal pixel values in the digital elevation model DEM are corrected according to the correction values of the abnormal pixel values to obtain a corrected digital elevation model DEM; specifically:

[0020] The abnormal pixel value a in the digital elevation model DEM i Replace with c i .

[0021] Furthermore, the first neural network model includes, in sequence, a first convolution module, a second convolution module, a third convolution module, a flattening layer, a first BP layer, a first Dropout layer, a second BP layer, a second Dropout layer, and a regression layer;

[0022] The pixel values in the corrected digital elevation model (DEM) are used as the input of the first convolution module;

[0023] The output of the first convolution module is used as the input of the second convolution module;

[0024] The output of the second convolution module is used as the input of the third convolution module;

[0025] The output of the third convolutional module is used as the input of the flattening layer;

[0026] Use the output of the flattening layer as the input of the first BP layer;

[0027] The output of the first BP layer is used as the input of the first Dropout layer;

[0028] The output of the first Dropout layer is used as the input of the second BP layer;

[0029] The output of the second BP layer is used as the input of the second Dropout layer;

[0030] The output of the second Dropout layer is used as the input of the regression layer, and the elevation value of each pixel in the final digital elevation model DEM is output through the regression layer.

[0031] Furthermore, the first convolution module includes a convolution layer, a pooling layer, a batch normalization layer and a Relu activation function layer in sequence, the input of the first convolution module is used as the input of the convolution layer, the output of the convolution layer is used as the input of the pooling layer, the output of the pooling layer is used as the input of the batch normalization layer, and the output of the batch normalization layer is used as the input of the Relu activation function layer, and the output of the Relu activation function layer is used as the output of the first convolution module;

[0032] The structures and working processes of the second convolution module and the third convolution module are the same as those of the first convolution module.

[0033] Furthermore, the second neural network model includes a multi-scale feature extraction module and a long short-term memory module based on an attention mechanism, wherein:

[0034] The multi-scale feature extraction module includes a first convolution unit, a second convolution unit and a third convolution unit;

[0035] The input of the second neural network model is used as the input of the first convolution unit;

[0036] The output of the first convolution unit is used as the input of the second convolution unit;

[0037] The output of the second convolution unit is used as the input of the third convolution unit;

[0038] The long short-term memory module based on the attention mechanism includes a first long short-term memory unit, a second long short-term memory unit, and a third long short-term memory unit;

[0039] The output of the first convolutional unit is used as the input of the first long short-term memory unit;

[0040] The output of the second convolutional unit is used as the input of the second long short-term memory unit;

[0041] The output of the third convolutional unit is used as the input of the third long short-term memory unit;

[0042] The features output by the first long short-term memory unit, the second long short-term memory unit, and the third long short-term memory unit are then fused to obtain spectral features.

[0043] Furthermore, the working process of the first convolution unit is:

[0044] In the first convolution unit, the input image passes through the convolution layer and the maximum pooling layer in sequence, and the output of the maximum pooling layer is used as the output of the first convolution unit;

[0045] The working processes of the first convolution unit and the second convolution unit are the same as the working process of the first convolution unit.

[0046] Furthermore, the first long short-term memory unit includes a long short-term memory network, a first fully connected layer, a tanh activation function layer, a second fully connected layer and a softmax activation function layer.

[0047] Furthermore, the working process of the first long short-term memory unit is:

[0048] First, the eigenvector of each time step is output through the long short-term memory network, and then the eigenvectors of different time steps are spliced into a feature matrix;

[0049] The feature matrix then passes through the first fully connected layer, the tanh activation function layer, and the second fully connected layer in sequence;

[0050] The output of the second fully connected layer is used as the input of the softmax activation function layer, and the weight vector is output through the softmax activation function layer;

[0051] Perform weighted summation of the feature vector of each time step and the weight vector to obtain the feature output by the first long short-term memory unit;

[0052] The working processes of the second long short-term memory unit and the third long short-term memory unit are the same as the working process of the first long short-term memory unit.

[0053] Furthermore, the final digital elevation model DEM is visualized.

[0054] The beneficial effects of the present invention are:

[0055] The present invention obtains a digital elevation model (DEM), a digital elevation model (INSAR-DEM), and a Landsat 8 satellite remote sensing hyperspectral image of the area to be classified. A final corrected digital elevation model (DEM) can be obtained based on the digital elevation model (DEM) and the digital elevation model (INSAR-DEM). Classification is then performed in combination with the elevation features of the final digital elevation model (DEM) and the spectral features of the image. Since the classification process of the present invention takes elevation information into account, the classification accuracy is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The present invention is a flowchart of a remote sensing hyperspectral image classification method based on digital elevation model feature extraction. DETAILED DESCRIPTION

[0057] Specific implementation method 1: Combination Figure 1 This embodiment describes a remote sensing hyperspectral image classification method based on digital elevation model feature extraction, which specifically includes the following steps:

[0058] Step 1: Obtain the digital elevation model (DEM), digital elevation model (INSAR-DEM) and Landsat8 satellite remote sensing hyperspectral image of the area to be classified;

[0059] Step 2: According to the digital elevation model (DEM) and the digital elevation model (INSAR-DEM), the abnormal pixel values in the digital elevation model (DEM) are determined, and the abnormal pixel values in the digital elevation model (DEM) are corrected according to the correction values of the abnormal pixels to obtain a corrected digital elevation model (DEM);

[0060] Step 3: Using the corrected digital elevation model DEM obtained in step 2 as input to the first neural network model, and outputting the final digital elevation model DEM through the first neural network model;

[0061] Step 4: Use the Landsat 8 satellite remote sensing hyperspectral image as the input of the second neural network model to extract the spectral features of the image;

[0062] Step 5: Extract elevation features from the final digital elevation model (DEM), and then combine the elevation features with the spectral features to obtain combined features.

[0063] The combined features are input into the support vector machine, and the classification results are output through the support vector machine.

[0064] The present invention first obtains a digital elevation model (DEM), a digital elevation model (INSAR-DEM), and a Landsat 8 satellite remote sensing hyperspectral image of the area to be classified, then obtains a final corrected digital elevation model (DEM) based on the digital elevation model (DEM) and the digital elevation model (INSAR-DEM), and finally, performs classification based on the elevation features of the final digital elevation model (DEM) and the spectral features of the image. Since the classification process of the present invention takes elevation information into consideration, the classification accuracy is effectively improved.

[0065] Specific embodiment 2: This embodiment differs from the specific embodiment 1 in that the abnormal pixel values in the digital elevation model DEM are determined based on the digital elevation model DEM and the digital elevation model INSAR-DEM; specifically:

[0066] Step 1: Obtain the corresponding pixel value of each pixel value in the digital elevation model DEM in the digital elevation model INSAR-DEM, and convert the pixel value a in the digital elevation model DEM into i The corresponding pixel value in the digital elevation model INSAR-DEM is recorded as b i , i = 1, 2, ..., n, n is the total number of pixel values in the digital elevation model DEM, that is, the total number of pixel values in the digital elevation model INSAR-DEM;

[0067] Step 2: Calculate a i and b i The average value c i , i=1,2,…,n;

[0068] Step 3: Calculate the deviation value |a i -c i |, |·| represent taking absolute values;

[0069] If the deviation value |a i -c i | is greater than the set threshold, then a i is the abnormal pixel value in the digital elevation model DEM;

[0070] If the deviation value |a i -c i | is less than or equal to the set threshold, then a i is the normal pixel value in the digital elevation model DEM;

[0071] Step 4: c i As the abnormal pixel value a i Correction value.

[0072] Other steps and parameters are the same as those in the first embodiment.

[0073] Specific embodiment three: This embodiment differs from specific embodiment one or two in that the abnormal pixel values in the digital elevation model DEM are corrected according to the correction values of the abnormal pixel values to obtain a corrected digital elevation model DEM; specifically:

[0074] The abnormal pixel value a in the digital elevation model DEM i Replace with c i .

[0075] Other steps and parameters are the same as those in the first or second embodiment.

[0076] Specific embodiment 4: This embodiment is different from any one of specific embodiments 1 to 3 in that the first neural network model sequentially includes a first convolution module, a second convolution module, a third convolution module, a flattening layer, a first BP layer, a first Dropout layer, a second BP layer, a second Dropout layer and a regression layer;

[0077] The pixel values in the corrected digital elevation model (DEM) are used as the input of the first convolution module;

[0078] The output of the first convolution module is used as the input of the second convolution module;

[0079] The output of the second convolution module is used as the input of the third convolution module;

[0080] The output of the third convolutional module is used as the input of the flattening layer;

[0081] Use the output of the flattening layer as the input of the first BP layer;

[0082] The output of the first BP layer is used as the input of the first Dropout layer;

[0083] The output of the first Dropout layer is used as the input of the second BP layer;

[0084] The output of the second BP layer is used as the input of the second Dropout layer;

[0085] The output of the second Dropout layer is used as the input of the regression layer, and the elevation value of each pixel in the final digital elevation model DEM is output through the regression layer.

[0086] The other steps and parameters are the same as those in the first to third embodiments.

[0087] Specific embodiment five: This embodiment differs from any one of specific embodiments one to four in that the first convolution module sequentially includes a convolution layer, a pooling layer, a batch normalization layer, and a Relu activation function layer, and the input of the first convolution module is used as the input of the convolution layer, the output of the convolution layer is used as the input of the pooling layer, the output of the pooling layer is used as the input of the batch normalization layer, and the output of the batch normalization layer is used as the input of the Relu activation function layer, and the output of the Relu activation function layer is used as the output of the first convolution module;

[0088] The structures and working processes of the second convolution module and the third convolution module are the same as those of the first convolution module.

[0089] The other steps and parameters are the same as those in the first to fourth embodiments.

[0090] Specific embodiment 6: This embodiment differs from any one of specific embodiments 1 to 5 in that the second neural network model includes a multi-scale feature extraction module and a long short-term memory module based on an attention mechanism, wherein:

[0091] The multi-scale feature extraction module includes a first convolution unit, a second convolution unit and a third convolution unit;

[0092] The input of the second neural network model is used as the input of the first convolution unit;

[0093] The output of the first convolution unit is used as the input of the second convolution unit;

[0094] The output of the second convolution unit is used as the input of the third convolution unit;

[0095] The long short-term memory module based on the attention mechanism includes a first long short-term memory unit, a second long short-term memory unit, and a third long short-term memory unit;

[0096] The output of the first convolutional unit is used as the input of the first long short-term memory unit;

[0097] The output of the second convolutional unit is used as the input of the second long short-term memory unit;

[0098] The output of the third convolutional unit is used as the input of the third long short-term memory unit;

[0099] The features output by the first long short-term memory unit, the second long short-term memory unit, and the third long short-term memory unit are then fused to obtain spectral features.

[0100] The other steps and parameters are the same as those in the first to fifth embodiments.

[0101] After extracting spectral features of different scales, the present invention fuses the extracted features, so that the classification result comprehensively considers the spectral features of different scales, thereby improving the classification accuracy.

[0102] Specific embodiment 7: This embodiment differs from any one of specific embodiments 1 to 6 in that the working process of the first convolution unit is as follows:

[0103] In the first convolution unit, the input image passes through the convolution layer and the maximum pooling layer in sequence, and the output of the maximum pooling layer is used as the output of the first convolution unit;

[0104] The working processes of the first convolution unit and the second convolution unit are the same as the working process of the first convolution unit.

[0105] The other steps and parameters are the same as those in the first to sixth embodiments.

[0106] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that the first long short-term memory unit includes a long short-term memory network, a first fully connected layer, a tanh activation function layer, a second fully connected layer and a softmax activation function layer.

[0107] The other steps and parameters are the same as those in the first to seventh embodiments.

[0108] Specific embodiment 9: This embodiment differs from any one of specific embodiments 1 to 8 in that the working process of the first long short-term memory unit is as follows:

[0109] First, the eigenvector of each time step is output through the long short-term memory network, and then the eigenvectors of different time steps are spliced into a feature matrix;

[0110] The feature matrix then passes through the first fully connected layer, the tanh activation function layer, and the second fully connected layer in sequence;

[0111] The output of the second fully connected layer is used as the input of the softmax activation function layer, and the weight vector is output through the softmax activation function layer;

[0112] Perform weighted summation of the feature vector of each time step and the weight vector to obtain the feature output by the first long short-term memory unit;

[0113] The working processes of the second long short-term memory unit and the third long short-term memory unit are the same as the working process of the first long short-term memory unit.

[0114] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0115] Specific embodiment ten: This embodiment differs from any one of specific embodiments one to nine in that the final digital elevation model DEM is visualized.

[0116] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0117] The above examples are merely illustrative of the calculation model and process of the present invention and are not intended to limit the embodiments of the present invention. Persons skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. This list of embodiments is not exhaustive; however, any obvious variations or modifications derived from the technical solution of the present invention remain within the scope of protection of the present invention.

Claims

1. A remote sensing hyperspectral image classification method based on digital elevation model feature extraction, characterized in that: The method specifically comprises the following steps: Step 1: Obtain the digital elevation model (DEM), digital elevation model (INSAR-DEM) and Landsat 8 satellite remote sensing hyperspectral image of the area to be classified; Step 2: According to the digital elevation model (DEM) and the digital elevation model (INSAR-DEM), the abnormal pixel values in the digital elevation model (DEM) are determined, and the abnormal pixel values in the digital elevation model (DEM) are corrected according to the correction values of the abnormal pixels to obtain a corrected digital elevation model (DEM); The method of determining abnormal pixel values in the digital elevation model DEM based on the digital elevation model DEM and the digital elevation model INSAR-DEM is as follows: Step 1: Obtain the corresponding pixel value of each pixel value in the digital elevation model DEM in the digital elevation model INSAR-DEM, and convert the pixel value a in the digital elevation model DEM into i The corresponding pixel value in the digital elevation model INSAR-DEM is recorded as b i , i=1,2,…,n, n is the total number of pixel values in the digital elevation model DEM, that is, the total number of pixel values in the digital elevation model INSAR-DEM; Step 2: Calculate a i and b i The average value c i , i=1,2,…,n; Step 3: Calculate the deviation value |a i -c i |, |·| represent taking absolute values; If the deviation value |a i -c i | is greater than the set threshold, then a i is the abnormal pixel value in the digital elevation model DEM; If the deviation value |a i -c i | is less than or equal to the set threshold, then a i is the normal pixel value in the digital elevation model DEM; Step 4: c i As the abnormal pixel value a i Correction value of Step 3: Using the corrected digital elevation model DEM obtained in step 2 as input to the first neural network model, and outputting the final digital elevation model DEM through the first neural network model; Step 4: Use the Landsat 8 satellite remote sensing hyperspectral image as the input of the second neural network model to extract the spectral features of the image; Step 5: Extract elevation features from the final digital elevation model (DEM), and then combine the elevation features with the spectral features to obtain combined features. The combined features are input into the support vector machine, and the classification results are output through the support vector machine.

2. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 1 is characterized in that: The abnormal pixel values in the digital elevation model DEM are corrected according to the correction values of the abnormal pixels to obtain the corrected digital elevation model DEM; specifically: The abnormal pixel value a in the digital elevation model DEM i Replace with c i .

3. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 2 is characterized in that: The first neural network model includes a first convolution module, a second convolution module, a third convolution module, a flattening layer, a first BP layer, a first Dropout layer, a second BP layer, a second Dropout layer and a regression layer in sequence; The pixel values in the corrected digital elevation model (DEM) are used as the input of the first convolution module; The output of the first convolution module is used as the input of the second convolution module; The output of the second convolution module is used as the input of the third convolution module; The output of the third convolutional module is used as the input of the flattening layer; Use the output of the flattening layer as the input of the first BP layer; The output of the first BP layer is used as the input of the first Dropout layer; The output of the first Dropout layer is used as the input of the second BP layer; The output of the second BP layer is used as the input of the second Dropout layer; The output of the second Dropout layer is used as the input of the regression layer, and the elevation value of each pixel in the final digital elevation model DEM is output through the regression layer.

4. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 3 is characterized in that: The first convolution module includes a convolution layer, a pooling layer, a batch normalization layer and a Relu activation function layer in sequence. The input of the first convolution module is used as the input of the convolution layer, the output of the convolution layer is used as the input of the pooling layer, the output of the pooling layer is used as the input of the batch normalization layer, and the output of the batch normalization layer is used as the input of the Relu activation function layer. The output of the Relu activation function layer is used as the output of the first convolution module; The structures and working processes of the second convolution module and the third convolution module are the same as those of the first convolution module.

5. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 4 is characterized in that: The second neural network model includes a multi-scale feature extraction module and a long short-term memory module based on an attention mechanism, wherein: The multi-scale feature extraction module includes a first convolution unit, a second convolution unit and a third convolution unit; The input of the second neural network model is used as the input of the first convolution unit; The output of the first convolution unit is used as the input of the second convolution unit; The output of the second convolution unit is used as the input of the third convolution unit; The long short-term memory module based on the attention mechanism includes a first long short-term memory unit, a second long short-term memory unit, and a third long short-term memory unit; The output of the first convolutional unit is used as the input of the first long short-term memory unit; The output of the second convolutional unit is used as the input of the second long short-term memory unit; The output of the third convolutional unit is used as the input of the third long short-term memory unit; The features output by the first long short-term memory unit, the second long short-term memory unit, and the third long short-term memory unit are then fused to obtain spectral features.

6. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 5 is characterized in that: The working process of the first convolution unit is: In the first convolution unit, the input image passes through the convolution layer and the maximum pooling layer in sequence, and the output of the maximum pooling layer is used as the output of the first convolution unit; The working processes of the first convolution unit and the second convolution unit are the same as the working process of the first convolution unit.

7. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 6 is characterized in that: The first long short-term memory unit includes a long short-term memory network, a first fully connected layer, a tanh activation function layer, a second fully connected layer and a softmax activation function layer.

8. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 7 is characterized in that: The working process of the first long short-term memory unit is as follows: First, the eigenvector of each time step is output through the long short-term memory network, and then the eigenvectors of different time steps are spliced into a feature matrix; The feature matrix then passes through the first fully connected layer, the tanh activation function layer, and the second fully connected layer in sequence; The output of the second fully connected layer is used as the input of the softmax activation function layer, and the weight vector is output through the softmax activation function layer; Perform weighted summation of the feature vector of each time step and the weight vector to obtain the feature output by the first long short-term memory unit; The working processes of the second long short-term memory unit and the third long short-term memory unit are the same as the working process of the first long short-term memory unit.

9. The remote sensing hyperspectral image classification method based on digital elevation model feature extraction according to claim 8, characterized in that: The final digital elevation model (DEM) is visualized.

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