Abstract

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'.

Highlights

  • A hyperspectral sensor obtains approximately continuous spectral curve of the ground object in ultraviolet, visible, near infrared, mid infrared, and other electromagnetic wave bands

  • From the comparison results of the evaluation indexes among three methods for the hyperspectral remote sensing image, it can be seen that the QGASR-convolutional neural network (CNN) is superior to the principal components analysis (PCA)-CNN and sparse representation (SR)-CNN in classification accuracy and kappa coefficient

  • A new hyperspectral remote sensing image classification method based on sparse representation with quantum genetic algorithm and convolutional neural network, namely QGASR-CNN is proposed in this paper

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Summary

INTRODUCTION

A hyperspectral sensor obtains approximately continuous spectral curve of the ground object in ultraviolet, visible, near infrared, mid infrared, and other electromagnetic wave bands. It obtains the spectral vectors of each pixel, extracts the local spectral information on the spectral vector by using CNN convolution layers, and takes the feature map generated by convolution operation as the input of the full connection layer, and completes the hyperspectral remote sensing image classification This method makes use of CNN’s local connection, weight sharing and other characteristics, greatly reduces the model parameters and training difficulty, further improves the classification performance, but fails to make full use of the rich space spectrum information of hyperspectral remote sensing image, reducing the ability of feature learning. In view of the characteristics of hyperspectral remote sensing image, such as the number of bands, the combination of atlas, information redundancy, spectral mixing and data nonlinearity, sparse representation with quantum genetic algorithm and convolutional neural network are integrated in order to propose a novel QGASR-CNN method, which is used to effectively describe the features and achieve The classification for hyperspectral remote sensing image.

GUANTUM GENETIC ALGORITHM
SPARSE REPRESENTATION THEORY
OVERVIEW OF QGASR-CNN
THE REALIZATION OF QGASR-CNN
Findings
CONCLUSION
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