A New Heterogeneity Scale to Improve Anisotropic Diffusion Based Speckle Filters in SAR Images

摘要

The noise like quality characteristic of SAR images known as speckle become most critical impediment for automatic segmentation and classification of targets. For last few decades many adaptive speckle filters were proposed. Most of these classical filters are single stage i.e. repeated use of them causes blurring of image features (e.g. edges or textures) and generates artefacts. But iterative uses of filters are required for desired amount of smoothing. To retain structure in adaptive filtering, key component is precise estimation of scene heterogeneity. Failure of traditional filters to retain features upon iteration is due to failure to measure scene heterogeneity optimally. Perona and Malik proposed an Anisotropic Diffusion (AD) equation which iteratively diffuses gray values by preserving edges. Black et al developed a robust statistical interpretation of AD, and opened a broader context to choose between alternative diffusion functions. Depending on Black's work earlier we proposed a robust speckle reducing anisotropic diffusion (ROSRAD) filter and also a heterogeneity scale based on Otsu's thresholding algorithm. This scale is optimal but not robust. This paper is an extension to our work, here we propose a different heterogeneity scale which is robust and performs better for speckle noise distribution.