Research on GF-1 remote sensing IHS image fusion algorithm based on compressed sensing
MA Ruiqi1, CHENG Bo2, LIU Xu’nan3, LIU Yueming2,4, JIANG Wei2,4, YANG Chen5
1. School of Lisiguang, China University of Geosciences(Wuhan), Wuhan 430074, China; 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 3. National Marine Hazard Mitigation Service, Beijing 100194, China; 4. University of Chinese Academy of Sciences, Beijing 100049,China; 5. Faculty of Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China
Abstract：According to characteristics of GF-1 remote sensing images with high spatial resolution, the authors used compressed sensing theory to improve the traditional IHS image fusion algorithm. The component I from IHS transform and panchromatic images used sparse matrix and measure matrix, the weighted average and OMP yielded new component I'. Finally, through an inverse IHS transform the result image was obtained. Combined with five quantitative indexes, analysis and evaluation were conducted. Experimental results show that, compared with the traditional methods, IHS fusion algorithm combined with compression perception can obtain a higher and less distorted correlation coefficient, and the fusion results not only have higher spatial information richness, but also maintain the color information of multi-spectral images. It may provide a reference to GF-1 image visual solutions for translation and image classification.
马瑞琪, 程博, 刘旭楠, 刘岳明, 江威, 杨晨. 基于压缩感知的GF-1遥感影像IHS融合算法[J]. 国土资源遥感, 2017, 29(4): 26-32.
MA Ruiqi, CHENG Bo, LIU Xu’nan, LIU Yueming, JIANG Wei, YANG Chen. Research on GF-1 remote sensing IHS image fusion algorithm based on compressed sensing. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 26-32.
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2017, Vol. 29 
