{'fraction_non_alphanumeric': 0.014480408858603067, 'fraction_numerical': 0.0, 'mean_word_length': 5.952662721893491, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Covariance-based texture description from weighted coherency matrix and gradient tensors for polarimetric SAR image classification

The present paper proposes a texture-based unsupervised classification algorithm for fully polarimetric SAR (PolSAR) images. Here, the main motivation is to combine polarimetric information and local structure gradients from PolSAR image data to describe textural features and then use them for classification purpose. In this work, the notion of PolSAR image textures is characterized by two key features. First, the polarimetric coherency matrix is estimated using a weighted averaging operator based on patch similarity. Second, the image local geometry is taken into account by exploiting the structure gradient tensors. These characteristics are then integrated into texture descriptors via the approach of covariance matrix. Unsupervised classification stage is finally achieved by employing an adapted distance measure for covariance-based descriptors. Experiments performed on very high resolution complex PolSAR images using the proposed algorithm provide very promising results in terms of terrain classification and discrimination.