Objective

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Classification is a major research task in hyperspectral remote sensing image processing. Extracting appropriate features from hyperspectral remote sensing images is a prerequisite to ensure the accuracy and effectiveness of classification. Traditional feature extraction methods can only extract single

shallow spectral or spatial features and have difficulty extracting deep spatial-spectral features. This limitation results in poor classification accuracy. Compared with traditional classification methods

hyperspectral image classification method based on deep learning model can extract deeper features of hyperspectral image. However

the existing deep learning methods can classify hyperspectral images but have low classification accuracy because of their simple network structure and insufficient feature extraction. Moreover

the parameters of deep network are hundreds of thousands of millions

which need a large number of labeled training samples and longer time to complete the model training. However

manual annotation of hyperspectral images is a time-consuming and laborious project

and only a few labeled samples can usually be used. Therefore

constructing a depth network model for hyperspectral image classification with a small number of training samples is a key research problem. A data expansion method of stacking pixel spatial transformation information is proposed to address the problems of simple network structure and insufficient feature extraction of existing classification methods based on deep learning. A hyperspectral image classification model based on different scales of dual-channel 3D convolutional neural network is also proposed to extract the essential spatial features of hyperspectral image.

Method

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On the one hand

the stacking space transform information method is used to expand the training samples for solving the problem of insufficient training samples in the process of hyperspectral image classification. On the other hand

the dual-channel convolutional neural network is used

and its overall structure is composed of preprocessing

data expansion

and dual-channel convolutional neural network (DC-CNN). First

the hyperspectral image is preprocessed to transform the pixels into 3×3 and 5×5 pixel blocks containing certain spatial information as the unit to be processed. Then

the unit to be processed uses the data expansion method of stacking spatial transformation information to expand the data. The expanded dataset is transformed into shape to meet the input dimension of dual-channel convolutional neural network. Then

the pixel blocks of different sizes are input into the branch convolution of different scales of DC-CNN

and they are combined with the spatial spectrum features extracted from the two channels for training to improve the generalization ability of the network. The spatial-spectral features of hyperspectral images can be more comprehensively and fully extracted using different convolution branches to process different sizes of pixel blocks. The information loss caused by dimension reduction can be reduced

and the classification accuracy can be improved. The method of stack space transform information enriches the potential space information of the center pixel and expands the dataset by rotating each pixel and its adjacent pixels

row column transform

and other operations. The expanded pixel blocks are input into dual-channel 3D convolutional neural networks with different scales to learn the deep features of the training set and achieve higher accuracy classification. Three popular hyperspectral image classification methods are selected and compared with this method to verify its effectiveness. The three methods are 2D convolutional neural network (2D-CNN)

3D convolutional neural network (3D-CNN)

and siamese neural network (SNN). The network structure of 3D-CNN is the same as that of 3×3 branches of DC-CNN

which can be considered a single-channel network model of DC-CNN.

Result

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Experimental results show that the Indian Pines and Pavia University datasets achieve 98.34% and 99.63% of the overall classification accuracy

respectively. The running time of different algorithms is also compared. The running time of the proposed algorithm is relatively shorter than that of the comparison algorithm

and this feature ensures the efficiency of the classification model. In addition

this study analyzes the influence of different convolution layers on the classification performance. It verifies that the convolution neural network composed of five convolution layers has the strongest feature extraction ability for hyperspectral images. The proposed method also has good classification performance on two groups of real hyperspectral image datasets. Specifically

the proposed method has certain generalization ability and advantages in running time. In this study

dual-channel convolution neural network is used to complete the deep spatial-spectral feature fusion of hyperspectral image

which fully extracts the hidden information of hyperspectral image and is conducive to the application of deep learning model in hyperspectral image classification. In the follow-up research

we can further explore the effect of multichannel convolution neural network

such as three- and four-channel convolution neural networks in hyperspectral image classification. The application of deep learning method in hyperspectral image can also be promoted.

Conclusion

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The performance of hyperspectral image classification model with limited training samples is not ideal. Thus

this study proposes a data expansion method of stacking pixel neighborhood spatial information and designs a hyperspectral image classification method based on dual-channel convolutional neural network

which is called DC-CNN. The data expansion method of stacking pixel neighborhood spatial information conducts spatial rotation and transformation on different scale pixel blocks to expand the number of training samples and obtain the hidden spatial information of the center pixel. This method solves the problem of insufficient training samples of depth model and insufficient spatial information extraction of hyperspectral image in feature extraction process. It inputs pixel blocks of different scales through DC-CNN network framework to comprehensively extract more abundant spatial information

completes the deep spatial-spectral information extraction

and inputs the fused spatial spectral features into softmax classifier to achieve more accurate classification.