Hyperspectral Remote Sensing Image Classification With CNN Based on Quantum Genetic-Optimized Sparse Representation

Due to the characteristics of the spectrum integration, information redundancy, spectrum mixing phenomenon and nonlinearity of the hyperspectral remote sensing images, it is a major challenging task to classify the hyperspectral remote sensing images. Therefore, a hyperspectral remote sensing image classification method, named QGASR-CNN is proposed in this paper. In the QGASR-CNN, a quantum genetic-optimized sparse representation method is designed to obtain the over-complete dictionary with sparsity, and achieve the feature sparse representation to construct the sparse feature matrix of hyperspectral remote sensing image pixel groups. Then the convolution neural network(CNN) directly convolutes with image pixels to build the feature mapping relation by using convolution operation. Finally, in order to testify the effectiveness of the QGASR-CNN, the actual hyperspectral remote sensing image datasets are selected in here. The comparison results show that the QGASR-CNN sparsely represents the features of hyperspectral remote sensing images and improves the classification accuracy. It can effectively alleviate the problems of the small samples and `salt and pepper misclassification'.