Stereo Matching Algorithm for High-Resolution Remote Sensing Images Based on Sparse Coding and Dictionary Learning

2021 ◽  
Author(s):  
Dongyang Liu ◽  
Junping Zhang ◽  
Youliang Guo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Dictionary Learning ◽  
Sparse Coding ◽  
Stereo Matching ◽  
Remote Sensing Images ◽  
Matching Algorithm

Study on Remote Sensing Images of De-Blurring Based on Dictionary Learning

2015 ◽  
Vol 738-739 ◽  
pp. 217-222
Author(s):  
Yan Jia ◽  
Zhen Tao Qin ◽  
Bang Xin Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Dictionary Learning ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Research Field ◽  
Projection Algorithm ◽  
Remote Sensing Images ◽  
Novel Algorithm

De-blurring the high resolution remote sensing images is an important issue in the relative research field of remote sensing. In this paper a novel algorithm of de-blurring the high resolution remote sensing images is proposed based on sparse representation. The high spatial resolution remote sensing images can be de-blurred by gradient projection algorithm, and keep the useful information of the image. The experimental results of the remote sensing images obtained by “the first satellite of high resolution” show that the algorithm can de-blur the image more effectively and improve the PSNR, this method has better performance than other dictionary learning algorithm.

scholarly journals A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

2021 ◽  
Vol 13 (10) ◽  
pp. 1903
Author(s):  
Zhihui Li ◽  
Jiaxin Liu ◽  
Yang Yang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Stereo Matching ◽  
Mean Shift ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Edge Matching ◽  
Refinement Method ◽  
Initial Segmentation ◽  
Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

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