A KPCA texture feature model for efficient segmentation of RADARSAT-2 SAR sea ice imagery

Sea ice information obtained from synthetic aperture radar (SAR) images is crucial for ensuring safe marine navigation and supporting climate change studies in polar regions. We propose a kernel principal component analysis (KPCA) local texture feature model for efficient sea ice segmentation. The proposed KPCA texture feature model is significant for several reasons. First, it takes into account the multiplicative nature of SAR speckle noise. The resulting KPCA features therefore assume independent and identically distributed (i.i.d.) Gaussian-like noise, which satisfies the assumptions inherent in classical statistical models such as the k-means algorithm. Second, the KPCA features are compact and statistically independent and therefore capable of reducing data redundancy. Third, the KPCA texture features are discriminative. Finally, the extraction of KPCA features requires less computation. Based on the KPCA model, a segmentation scheme is implemented in four steps. First, local patch-based texture descriptors are extracted from the image. Second, KPCA is performed on the patch-based texture descriptors to obtain compact and discriminative texture features with Gaussian-like noise characteristics. Third, the k-means algorithm is performed on extracted KPCA features to segment the image. Finally, a neighbourhood-based majority voting scheme is employed to determine the final label of each pixel. We compared the proposed method with several other popular sea ice segmentation approaches. The results demonstrated that our method is robust to speckle noise level, is accurate and fast and may thus better support the operational segmentation of sea ice in SAR imagery.