Speckle aware spatial search based segmentation algorithm for crop classification in SAR images using a three component K-NN model

Abstract

We provide a speckle aware image segmentation algorithm for synthetic aperture radar (SAR) data. It uses search based segmentation using a three-component machine learning model where speckle noise is considered as discrete component of the feature description. This method allows for the removal of the need for a de-speckling filter during the feature extraction process for SAR images, resulting in a more efficient and accurate approach. A three-component model is used to efficiently represent a feature in SAR data. The algorithm is used to segment different crops from Sentinel-1 C-band SAR data. We describe the search-based segmentation algorithm, three-component model, and its design using K-NN algorithm. We tested the proposed algorithm against K-NN based segmentation on Sentinel-1 images de-speckled using widely used Lee, Refine Lee, Frost, and Gamma-MAP filters. The proposed method is found to produce better classification accuracy compared to results from K-NN and commonly used de-speckling filters.