The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 319–323, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-319-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 319–323, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-319-2020

  21 Aug 2020

21 Aug 2020

AN IMPROVED SYNTHETIC APERTURE RADAR IMAGE DENOISING METHOD BASED ON BLOCK-MATCHING AND 3D FILTERING

X. Liu1,2, X. Lu1, and L. Ma1,2 X. Liu et al.
  • 1Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo 454003, China
  • 2School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

Keywords: Synthetic Aperture Radar, Speckle Noise, Denoising, Block-Matching And 3D Filtering, Block Similarity Measure

Abstract. Block-matching and 3D filtering (BM3D) are used to reduce the multiplicative coherent speckle noise of Synthetic Aperture Radar (SAR) images, which may lead to the loss of image details. This paper proposes an improved similarity metric BM3D algorithm. Firstly, this method analyses the coherent speckle noise model, and applies a logarithmic transformation to make the BM3D algorithm suitable for multiplicative noise. Secondly, this method based on the calculation method of Euclidean distance weights for similar image blocks, and the Pearson correlation coefficient is introduced to improve the similarity metric. The accuracy of similar image block matching is improved, which is beneficial for removing image noise and maintaining image information. The experiments in this paper compared the results of this method with Frost filtering, Kuan filtering, wavelet soft thresholding and SAR-BM3D filtering algorithms. The results were compared and analysed by subjective vision and objective indicators. The experimental results show that compared with other filtering algorithms, the proposed algorithm has better ability to reduce speckle noise and preserve edge detail information for the image.