Remote sensing image de-noising algorithm based on double discrete wavelet transform

doi:10.6046/gtzyyg.2015.04.03

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Abstract

In combination with discrete wavelet transform(DWT) and two-dimensional multi-stage median filtering(TMMF)algorithms, the author proposes a self-adaptive remote sensing image de-noising algorithm in this paper. Firstly, the remote sensing noise image is conducted with the single layer DWT so as to obtain the low-frequency wavelet sub-image and high-frequency wavelet sub-images. As the low-frequency sub-image is not polluted by noise, the low-frequency sub-image should be kept unchanged. Signal layer DWT is conducted again for high-frequency sub-images, and therefore the secondary low-frequency sub-image and secondary high-frequency sub-images are obtained. Then, the secondary low-frequency sub-images are filtered by the improved TMMF algorithm, and the secondary high-frequency sub-images are processed by the improved wavelet hard threshold function model. Finally, the wavelet reconstruction image is acquired. Three remote sensing images with detailed information were adopted to test the performance of the method proposed in this paper, and the results of theoretical analysis and test show that the filtering performance of the algorithm proposed in this paper is superior to TMMF algorithm and its improved algorithm as well as wavelet transform hard threshold de-noising algorithm.

Keywords lake morphology shape index fractal dimension lake district remote sensing

:	TP751.1
	TP391.41

Issue Date: 23 July 2015

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	LIU Lei
	ZANG Shuying
	SHAO Tiantian
	WEI Jinhong
	SONG Kaishan

Cite this article:

LIU Lei,ZANG Shuying,SHAO Tiantian, et al. Remote sensing image de-noising algorithm based on double discrete wavelet transform[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 14-20.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.04.03 OR https://www.gtzyyg.com/EN/Y2015/V27/I4/14

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