26 March 2014 Pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform
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Abstract
The goal of pan-sharpening is to get an image with higher spatial resolution and better spectral information. However, the resolution of the pan-sharpened image is seriously affected by the thin clouds. For a single image, filtering algorithms are widely used to remove clouds. These kinds of methods can remove clouds effectively, but the detail lost in the cloud removal image is also serious. To solve this problem, a pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform (NSST) is proposed. For the low-resolution multispectral (LR MS) and high-resolution panchromatic images with thin clouds, a mask dodging method is used to remove clouds. For the cloud removal LR MS image, an adaptive principal component analysis transform is proposed to balance the spectral information and spatial resolution in the pan-sharpened image. Since the clouds removal process causes the detail loss problem, a weight matrix is designed to enhance the details of the cloud regions in the pan-sharpening process, but noncloud regions remain unchanged. And the details of the image are obtained by NSST. Experimental results over visible and evaluation metrics demonstrate that the proposed method can keep better spectral information and spatial resolution, especially for the images with thin clouds.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Cheng Shi, Fang Liu, Ling-Ling Li, Hong-Xia Hao, "Pan-sharpening algorithm to remove thin cloud via mask dodging and nonsampled shift-invariant shearlet transform," Journal of Applied Remote Sensing 8(1), 083658 (26 March 2014). https://doi.org/10.1117/1.JRS.8.083658 . Submission:
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