Adaptive regularized scheme for remote sensing image fusion

doi:10.1007/s11707-015-0514-7

Front. Earth Sci.

2016, Vol. 10

Issue (2) : 236-244 https://doi.org/10.1007/s11707-015-0514-7

RESEARCH ARTICLE

Adaptive regularized scheme for remote sensing image fusion

Sizhang TANG^1,²,Chaomin SHEN¹,Guixu ZHANG^1,^*(

)

1. Shanghai Key Laboratory of Multidimensional Information Processing and Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China
2. Department of Information and Computer Science, Shanghai Business School, Shanghai 201400, China

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Abstract

We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a “grey world” assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation (TV)–L₁ term for edges and L₂-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler–Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.

Keywords remote sensing image fusion adaptive regulariser variational method steepest descent method

Corresponding Author(s): Guixu ZHANG

Just Accepted Date: 26 May 2015 Online First Date: 28 July 2015 Issue Date: 05 April 2016

Cite this article:

Sizhang TANG,Chaomin SHEN,Guixu ZHANG. Adaptive regularized scheme for remote sensing image fusion[J]. Front. Earth Sci., 2016, 10(2): 236-244.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-015-0514-7
https://academic.hep.com.cn/fesci/EN/Y2016/V10/I2/236

Fig.1 Edge?extraction?using?different?methods.?(a)?Original?image.?(b)–(g)?Edge-images?extracted?by?Sobel,?Prewitt,?Roberts, Canny, Kirsch, and the fused methods.

Fig.2 Input images and qualitative comparison. (a)–(b) Input images; (c)–(f) Fused images by Piella, Fang, Yuan, and the proposed method.

Tab.1 Comparison of results in Fig. 2

Fig.3 Input images and qualitative comparison. (a)–(b) Input images; (c)–(f) Fused images by Piella, Fang, Yuan and the proposed method.

Tab.2 Comparison of results in Fig. 3

Fig.4 Input images and qualitative comparison. (a)–(b) Input images; (c)–(j) fused images by Piella, Yuan, Fang, and the proposed method.

Tab.3 Comparison of fused results in Fig. 4

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