Reconstruction method of compressed sensing for remote sensing images cooperating with energy compensation

2016 ◽  
Author(s):  
Jinping He ◽  
Ningjuan Ruan ◽  
Haibo Zhao ◽  
Yuchen Liu
Keyword(s):  
Remote Sensing ◽  
Compressed Sensing ◽  
Reconstruction Method ◽  
Remote Sensing Images ◽  
Energy Compensation

Remote sensing images fusion based on block compressed sensing

2013 ◽  
Author(s):  
Sen-lin Yang ◽  
Guo-bin Wan ◽  
Bian-lian Zhang ◽  
Xin Chong
Keyword(s):  
Remote Sensing ◽  
Compressed Sensing ◽  
Remote Sensing Images ◽  
Block Compressed Sensing

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

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Sheng Cang ◽  
Achuan Wang
Keyword(s):  
Remote Sensing ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Prediction Algorithm ◽  
Remote Sensing Images ◽  
Difference Vector ◽  
The Difference ◽  
Hyperspectral Remote Sensing Images

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.

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images
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