Classification using polarimetric and interferometric SAR-data

M. Hellmann, S. R. Cloude, K. Papathanassiou
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引用次数: 8

Abstract

The investigation presented in this paper demonstrate a first order approach to an automatic classification and extraction of cartographic relevant features from SAR data. The authors propose a fusion of polarimetric and interferometric classification techniques that is able to solve several classification ambiguities which are not resolvable with one method alone and is also able to improve significantly the accuracy of the classification results. The complimentarity of the polarimetric and interferometric coherence based classification approaches and the improvements resulting from their combination are demonstrated using data from the space-shuttle-borne SIR-C/X-SAR radar system.
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利用偏振和干涉sar数据进行分类
本文的研究展示了一种从SAR数据中自动分类和提取地图相关特征的一阶方法。本文提出了一种融合偏振法和干涉法的分类技术,该技术能够解决单用一种方法无法解决的分类歧义,并能显著提高分类结果的准确性。利用航天飞机上的SIR-C/X-SAR雷达系统的数据,证明了基于偏振和干涉相干的分类方法的互补性以及它们结合所带来的改进。
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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.
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