Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

Cheng Shi; Fang Liu; Lingling Li; Licheng Jiao; Hongxia Hao; Ronghua Shang; Yangyang Li

doi:10.1117/1.JRS.12.016011

11 January 2018 Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

Cheng Shi, Fang Liu, Lingling Li, Licheng Jiao, Hongxia Hao, Ronghua Shang, Yangyang Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016011 (January 2018). https://doi.org/10.1117/1.JRS.12.016011

Abstract

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

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Cheng Shi, Fang Liu, Lingling Li, Licheng Jiao, Hongxia Hao, Ronghua Shang, and Yangyang Li "Pan-sharpening via compressed superresolution reconstruction and multidictionary learning," Journal of Applied Remote Sensing 12(1), 016011 (11 January 2018). https://doi.org/10.1117/1.JRS.12.016011

Received: 5 September 2017; Accepted: 15 December 2017; Published: 11 January 2018

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