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REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (4) : 14-20     DOI: 10.6046/gtzyyg.2015.04.03
Technology and Methodology |
Remote sensing image de-noising algorithm based on double discrete wavelet transform
Department of Information Management, Henan Vocational College of Economics and Trade, Zhengzhou 450018, China
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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  
Issue Date: 23 July 2015
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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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