Multi-feature fusion-based recognition and processing of impulse noise in remote sensing images

doi:10.6046/zrzyyg.2021319

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Abstract

Eliminating impulse noise of high-quality remote sensing images is of great significance for applied research. It has always been a challenge to eliminate high-density impulse noise while remaining detailed information on edges in original remote sensing images. This study concluded that uncertain changes will appear when a remote sensing image is corrupted by impulse noise. Given this, an uncertainty model based on the evidence theory was constructed using multiple features of impulse noise. The BJS divergence and the reliability entropy were fused into the model to obtain new weights and a new probability assignment. Then, the classification between noise and signals was given according to fusion rules and probability transformation, thus effectively reducing the possibility of high-level conflicts. The experimental results show that the classification method proposed in this study is effective even when the noise density is up to over 90% and can well maintain detailed information on different ground objects in the denoised remote sensing images.

Keywords evidence theory uncertainty modeling fusion rules highly conflict remote sensing image

ZTFLH:

TP391.41

Issue Date: 21 September 2022

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	Xiaojian MA
	Fashun ZHAO
	Yanbin LIU

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Xiaojian MA,Fashun ZHAO,Yanbin LIU. Multi-feature fusion-based recognition and processing of impulse noise in remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 17-26.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021319 OR https://www.gtzyyg.com/EN/Y2022/V34/I3/17

Fig.1 $d (I, [x, x])$ of pixels

Fig.2 The curve of relation between $r g$ and $S I M i j$

Fig.3 Modeling and fusion process of multi-feature criterion

Fig.4 Remote sensing images for different ground object

噪声密度/%	ASMF-DBMR		IBDND		本文算法
噪声密度/%	$S S I M$	$A R$ /%	$S S I M$	$A R$ /%	$S S I M$	$A R$ /%
10	0.980	99.61	0.981	99.43	0.981	99.65
20	0.958	99.59	0.930	99.38	0.960	99.33
30	0.932	99.57	0.784	99.20	0.926	99.38
40	0.899	99.52	0.548	98.48	0.896	99.48
50	0.863	99.51	0.315	96.93	0.851	99.62
60	0.817	99.48	0.166	94.12	0.804	99.76
70	0.750	99.59	0.089	89.59	0.756	99.76
80	0.655	99.62	0.045	84.14	0.678	99.73
90	0.531	99.82	0.018	75.81	0.553	99.84

Tab.1 AR and SSIM of different algorithms

Fig.5 Comparison between ASMF-DBER algorithm and the proposed algorithm at the noise level of 50%

Fig.6 Comparison of local information at the noise level of 50%

Tab.2 Comparison of filtering effect under 10%,30%,50%,70% and 90% noise density

Fig.7 Comparison of the PSNR of remote sensing images

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