A DCT-based adaptive compression algorithm customized for radar imagery

遥感信息 Pub Date : 1997-08-03 DOI:10.1109/IGARSS.1997.609183

A. Andreadis, G. Benelli, A. Garzelli, S. Susini

引用次数: 8

Abstract

An adaptive DCT-based image compression algorithm for radar images is proposed, tested and compared to JPEG and to classical coding algorithms for remote sensing imagery. The Modified Adaptive Discrete Cosine Transform (MADCT) scheme is proposed, which allows one to classify each image block by means of a threshold criterion based on AC and DC activity. The strategy of transmission of the DCT coefficients, the recovering process of blocks incorrectly discarded, and the bit-allocation phase have been properly designed to provide high compression of two classes of images: X-band real-aperture radar images for ship traffic control, and SAR images for browsing applications. The experimental results, in terms of PSNR and compression ratio, prove the superiority of the novel scheme with respect to standard coding techniques.

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一种基于dct的雷达图像自适应压缩算法

提出了一种基于dct的雷达图像自适应压缩算法，并与JPEG和经典的遥感图像编码算法进行了测试和比较。提出了一种改进的自适应离散余弦变换(MADCT)方案，该方案通过基于交流和直流活动的阈值准则对每个图像块进行分类。合理设计了DCT系数的传输策略、错误丢弃的块的恢复过程和位分配阶段，以提供两类图像的高压缩:用于船舶交通管制的x波段实孔径雷达图像和用于浏览应用的SAR图像。实验结果表明，在PSNR和压缩比方面，该方案相对于标准编码技术具有优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

遥感信息

自引率

0.00%

发文量

3984

期刊介绍： Remote Sensing Information is a bimonthly academic journal supervised by the Ministry of Natural Resources of the People's Republic of China and sponsored by China Academy of Surveying and Mapping Science. Since its inception in 1986, it has been one of the authoritative journals in the field of remote sensing in China.In 2014, it was recognised as one of the first batch of national academic journals, and was awarded the honours of Core Journals of China Science Citation Database, Chinese Core Journals, and Core Journals of Science and Technology of China. The journal won the Excellence Award (First Prize) of the National Excellent Surveying, Mapping and Geographic Information Journal Award in 2011 and 2017 respectively. Remote Sensing Information is dedicated to reporting the cutting-edge theoretical and applied results of remote sensing science and technology, promoting academic exchanges at home and abroad, and promoting the application of remote sensing science and technology and industrial development. The journal adheres to the principles of openness, fairness and professionalism, abides by the anonymous review system of peer experts, and has good social credibility. The main columns include Review, Theoretical Research, Innovative Applications, Special Reports, International News, Famous Experts' Forum, Geographic National Condition Monitoring, etc., covering various fields such as surveying and mapping, forestry, agriculture, geology, meteorology, ocean, environment, national defence and so on. Remote Sensing Information aims to provide a high-level academic exchange platform for experts and scholars in the field of remote sensing at home and abroad, to enhance academic influence, and to play a role in promoting and supporting the protection of natural resources, green technology innovation, and the construction of ecological civilisation.