Work

Nonlinear Quantization Method of SAR Images with SNR Enhancement and Segmentation Strategy Guidance

Year: 2025

Type: article

Abstract: The quantization process of synthetic aperture radar (SAR) images faces significant challenges due to their high dynamic range, resulting in notable quantization distortion. This not only degrades the visual quality of the quantized images but also severely impacts the accuracy of image interpretation. To mitigate the distortion caused by uniform quantization and enhance visual quality, this paper introduced a novel nonlinear quantization framework via signal-to-noise ratio (SNR) enhancement and segmentation strategy guidance. This framework introduces guiding information to improve quantization performance in weak scattering regions. A histogram adjustment method is developed to incorporate the spatial information of SAR images into the quantization process to enhance the quantization performance, specifically within weak scattering regions. Additionally, the optimal quantizer is improved by refining the SNR distribution across quantization units, addressing imbalances in their allocation. Experimental results based on Gaofen-3 (GF-3) satellite data demonstrate that the proposed algorithm approaches the global quantization performance of optimal quantizers while achieving superior local quantization performance compared to existing methods.

Source: Remote Sensing

DOI: 10.3390/rs17030557

Authors: Zijian Yao, Linlin Fang, Junxin Yang, Lihua Zhong

Institutions: Chinese Academy of Sciences, Aerospace Information Research Institute, University of Chinese Academy of Sciences

Language: English

Topic: Advanced Data Compression Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: Ministry of Science and Technology of the People's Republic of China, National Key Research and Development Program of China