Super-resolution fusion method for remote sensing image based on dictionary learning

doi:10.6046/gtzyyg.2017.01.08

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Abstract

In consideration of the fact that multi-source remote sensing image fusion is restricted by the existing resolution, the authors propose a super-resolution remote sensing image fusion method based on dictionary learning with sparse representation theory in this paper. The spatial resolution of multispectral images can be promoted to 1 or 2 times higher than the spatial resolution of panchromatic image. Under the framework of the method in remote sensing image fusion, a learning dictionary was established, the redundant dictionary on image sparse representation was used to conduct super-resolution reconstruction implementation. Then the Gram-Schmidt(GS) spectrum sharpening method was used as a fusion rule to obtain super resolution multispectral image fusion. Three experiments were carried out using QuickBird data. The results show that the proposed method is suitable for remote sensing image super-resolution fusion with some advantages in comparison with traditional fusion method, traditional super-resolution method and the other dictionary learning strategy. This paper provides a feasible solution for multi-source remote sensing image fusion, and has referential significance for other fusion methods.

Keywords urban areas extraction MHSI HSI DMSP-OLS

TP751.1

Issue Date: 23 January 2017

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	YANG Xiaonan
	XU Yun
	TIAN Yugang

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YANG Xiaonan,XU Yun,TIAN Yugang. Super-resolution fusion method for remote sensing image based on dictionary learning[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 50-56.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.08 OR https://www.gtzyyg.com/EN/Y2017/V29/I1/50

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