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国土资源遥感  2014, Vol. 26 Issue (3): 1-7    DOI: 10.6046/gtzyyg.2014.03.01
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高光谱图像异常目标检测算法研究与进展
成宝芝
大庆师范学院物理与电气信息工程学院, 大庆 163712
Study and progress of anomaly target detection in hyperspectral imagery
CHENG Baozhi
College of Physics and Electrical Information Engineering, Daqing Normal University, Daqing 163712, China
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摘要 

高光谱图像是一种新型的具有“图谱合一”特性的遥感图像,其连续的光谱曲线可更好地表达地表物质间的细微差异,在地表物质的分类、解混和目标探测等方面得到了广泛应用。随着高光谱遥感技术的深入发展,对不需要先验信息的异常目标检测的研究成为最活跃的方向之一,许多研究者提出了具有较好效果的异常检测算法。基于对国内外已有算法的综合归纳和分析,系统地论述了高光谱异常检测的研究现状和最新进展。阐述了高光谱异常目标检测的实质和基本理论;从算法思想、关键技术和优缺点等方面重点分析总结了较有代表性的异常目标检测算法,并对其进行了概括和阐述;最后对异常检测算法的未来研究方向进行了展望,力图为高光谱异常目标检测算法研究找到新的突破点。

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张修远
刘修国
关键词 随机森林(RF)模糊分类高维特征    
Abstract

Hyperspectral image is a new kind of remote sensing images with the feature of "combining mapping and spectra into one",thus better expressing the subtle differences on the surface of the material through the continuous spectral curve. Hyperspectral images have a wide range of applications in such aspects as classification,unmixing and target detection. With the continuous development of hyperspectral remote sensing technology,anomaly target detection has become one of the most active direction of research because it doesn't need a priori information. Many anomaly target detection algorithms have been proposed. Based on data available both in China and abroad,this paper summarized the research situation and new progress in anomaly detection algorithms. The author first expounded the essence of hyperspectral anomaly target detection and used the basic theory and then analyzed and summed up some representative anomaly detection algorithms in such aspects as the ideas of algorithm,key technology,advantages and disadvantages. On such a basis, the author summarized and described the evaluation method of anomaly detection and discussed the future development trend of anomaly target detection algorithm, with the purpose of finding new breakthroughs in the study of the algorithm of hyperspectral anomaly target detection.

Key wordsrandom forest(RF)    fuzzy classification    high dimensional features
收稿日期: 2013-07-09      出版日期: 2014-07-01
ZTFLH:  TP751.1  
基金资助:

大庆师范学院科学研究基金项目(编号:11ZR09)资助。

作者简介: 成宝芝(1976-),男,博士,讲师,主要从事高光谱图像处理研究。Email:chengbaozhigy@163.com。
引用本文:   
成宝芝. 高光谱图像异常目标检测算法研究与进展[J]. 国土资源遥感, 2014, 26(3): 1-7.
CHENG Baozhi. Study and progress of anomaly target detection in hyperspectral imagery. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 1-7.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.03.01      或      http://www.gtzyyg.com/CN/Y2014/V26/I3/1
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