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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 46-52     DOI: 10.6046/gtzyyg.2020.04.07
Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area
WANG Xiaobing1,2,3()
1. Sinosteel Maanshan General Institute of Mining Research Co., Ltd., Maanshan 243000, China
2. State Key Laboratory of Safety and Health for Metal Mines, Maanshan 243000, China
3. Huawei National Engineering Research Center of High Efficient Cyclic and Utilization of Metallic Mineral Resources Co., Ltd., Maanshan 243000, China
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Remote sensing image plays an important role in ecological restoration, geological disaster monitoring and prevention of mining area; nevertheless, due to the complex environment of mining area, the obtained remote sensing images of the mining area contains different kinds of intensity noise, which affects the subsequent image interpretation and analysis to a great extent. In this paper, the study ideas of image edge detection and noise suppression are effectively fused together, and the improved denoising method (LWT-IEDPO) of remote sensing image in mining area based on the fusion of lifting wavelet thresholding (LWT) and improved edge dectction of Prewitt operator (IEDPO) is proposed. According to the basic principal of the new method proposed in this paper, firstly, lifting wavelet transform is done for the original remote sensing image; under the condition that the low-frequency wavelet sub-band is left untreated, a two-parameters thresholding function model is designed for adaptive noise suppression of high-frequency sub-bands, and the remote sensing image after initial denoising is obtained by the operation of inverse lifting wavelet transform. Secondly, for the purpose of effectively enhancing the details of the filtered remote sensing image, the detection template of the classical Prewitt operator is extended to 6 directions of 0°, 30°, 60°, 90°, 120° and 150°, and the corresponding detection results fusion rules are proposed. The improved Prewitt operator is put forward to extract the image edge information of the filtered image, and the edge and non-edge image are obtained. Then, the visual effect non-edge image is further improved by adopting the improved Pal-King fuzzy algorithm. Finally, the goal of high definition restoration of the original remote sensing image is realized by the superimposition of the enhanced non-edge image and edge image. Based on MATLAB language, the proposed remote sensing image processing method is compared with the classical hard thresholding model, soft thresholding model and two existing improved wavelet thresholding algorithms; in addition, the two indices of peak signal to noise ratio (PSNR) and edge protection index (EPI) are used to conduct quantitative analysis and comparison of the performance of the above algorithms. The study results show that the goal of effectively filtering of noise remote sensing image can be realized effectively, and the overall performance of the proposed algorithm is better than that of the other four algorithms.

Keywords image processing      remote sensing image      lifting wavelet transform      Prewitt operator      image inhancement     
:  TP751  
Issue Date: 23 December 2020
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Xiaobing WANG
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Xiaobing WANG. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
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Fig.1  Expanded detection template of the Prewitt operator with six directions
Fig.2  Comparison of the test results of image 1
Fig.3  Comparison of the test results of image 2
遥感图像 高斯噪
图像1 0.10 23.345 24.480 26.603 28.135 28.305 0.678 8 0.703 4 0.808 6 0.828 7 0.863 2
0.15 21.056 22.305 25.559 26.406 27.002 0.599 0 0.609 8 0.775 4 0.769 3 0.810 5
0.20 19.807 20.209 20.698 23.790 25.329 0.506 7 0.598 8 0.620 1 0.670 4 0.755 6
图像2 0.10 24.478 25.387 26.668 28.090 28.669 0.700 9 0.675 9 0.795 6 0.821 4 0.864 7
0.15 22.006 23.076 24.490 25.308 26.365 0.574 3 0.620 3 0.603 2 0.706 5 0.795 9
0.20 18.565 19.690 21.212 22.210 24.497 0.489 6 0.556 9 0.573 3 0.601 2 0.703 1
Tab.1  Values of the evaluation indexes of the performance of filtering algorithms
Fig.4  Comparison of the filtering results of an open-pit stope image
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