Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction

Sheng Cang,Achuan Wang

doi:10.1155/2020/7160390

Abstract

Hyperspectral remote-sensing images have the characteristics of large transmission data and high propagation requirements, so they are faced with transmission and preservation problems in the process of transmission. In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. Firstly, according to the high spectral correlation of hyperspectral remote-sensing images, the hyperspectral images are grouped according to the band, and a standard band is determined in each group. The standard band in each group is weighted by the GISMT compressed sensing method. Then, a prediction model of the general band in each group is established to realize the remote-sensing image reconstruction in the general band. Finally, the difference between the actual measured value and the predicted value is calculated. According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. It is shown by experiments that this method can effectively improve the reconstruction effect of hyperspectral images.

Highlights

Hyperspectral images are a set of images with spectral resolutions in the order of 10− 2λ magnitude, which typically contain hundreds of spectral bands and each of which is imaged separately
The difference vector of the difference value is reconstructed, the predicted value is updated according to the vector, and the original image of the band is restored. is algorithm makes full use of the characteristics of hyperspectral correlation, reduces the amount of data acquisition, and through experimental verification, this algorithm can improve the effect of hyperspectral image reconstruction
The histogram of the gray distribution of the part is shown in Figure 1(b). e abscissa is the gray value of the image, and the ordinate is the frequency at which the grayscale appears

Summary

Introduction

Hyperspectral images are a set of images with spectral resolutions in the order of 10− 2λ magnitude, which typically contain hundreds of spectral bands and each of which is imaged separately. E GISMT algorithm is a kind of algorithm based on compression perception, compressed sensing theory as a new kind of effective mechanism of image acquisition and sampling method, which is different from traditional It has the following characteristics: (1) the signal sparse sex is no longer subject to the bandwidth size limit and depends on the sparse signal itself, namely, the content and structure of the important information in the signal; (2) signal sampling and compression processing are completed simultaneously; (3) while retaining complete key information, fewer measurements are obtained after sparse representation; and (4) it can reconstruct the original signal from a small number of measured values with high quality and express the signal structure completely. The difference vector of the difference value is reconstructed, the predicted value is updated according to the vector, and the original image of the band is restored. is algorithm makes full use of the characteristics of hyperspectral correlation, reduces the amount of data acquisition, and through experimental verification, this algorithm can improve the effect of hyperspectral image reconstruction

Background

The Algorithm of Image Reconstruction

Conclusion