Abstract
The resolution of SAR (synthetic-aperture radar) remote sensing images becomes higher to provide more details, but these images contain more data, which creates a limitation in terms of transport and storage. Most of existing image data compression frameworks are lossy or designed for spectral images. In this paper, we propose a novel lossless compression encoding framework for SAR remote sensing images. In the proposed framework, an outline of the image and the high-frequency components are separated and processed separately to increase the relativity of adjacent pixels. So the accuracy of prediction is improved, which makes the data compression more effective. The outline image is down-sampled to reduce data size, and the nonlocally centralized sparse representation-based super-resolution method is used to predict pixel values using the information in nonlocal similar regions. The proposed framework is evaluated with the ground range detected and PauliRGB images captured by SAR satellites. The results show that the proposed technique can get an efficient compression performance and it outperforms existing lossless compression frameworks in terms of compression efficiency.
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.
