Abstract
In this letter, a simple, yet very powerful local descriptor called local pattern descriptor (LPD) is proposed for synthetic aperture radar (SAR) images classification. The descriptor aims at exploiting the underlying properties of SAR image texture. Specifically, LPD consists of two parts: image quantization and statistical features extraction. The method of image quantization is based on recent local binary pattern. For an SAR image patch in a moving window, after quantization, different patterns can be obtained, which represent the local structures that exist in SAR image. Then, the statistical features extracted from the different patterns are concatenated to construct the LPD. Experiments on the TerraSAR-X image present that the proposed descriptor yields promising results for SAR image classification when compared to other widely used features.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
