Unsupervised Change Detection in Remote sensing Image Based on Image Fusion in Nonsubsampled Shearlet Transform Domain and fuzzy k-means clustering

2018 ◽  
Author(s):  
Duliang Lv ◽  
Feng Li ◽  
QingRui Guo ◽  
Xu Wang ◽  
Tao Chen
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Transform Domain ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform

scholarly journals Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1756
Author(s):  
Liangliang Li ◽  
Hongbing Ma
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
High Frequency ◽  
Rapid Development ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Saliency Map ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Remote Sensing Image Fusion

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

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