Particle Filter Sample Texton Feature for SAR Image Classification

This letter presents a novel approach to learning the correct sampling positions by introducing a particle filter. A filtering, labeling, and statistics framework we previously proposed is applied to construct a complete texture descriptor named particle filter sample texton (PFST) feature for the classification of synthetic aperture radar (SAR) images. First, the gray values of the key points tracked by a particle filter in the local image patch are concatenated into a vector. Second, the vectors are labeled using a texton dictionary clustered from the training images. Finally, the histogram statistics is performed on these labels to generate the feature vectors for classification. The proposed method is more robust in terms of speckle noise and extremely low signal-to-noise ratio than those of the existing fixed-point and random sampling methods that play a significant role in popular binary textural descriptors. The experiments conducted on the TerraSAR image present evidence that the key points tracked by the particle filter effectively preserve the texture information, and the PFST feature performs best in the extreme situations.