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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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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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关键词 随机森林(RF)模糊分类高维特征    

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
:  TP751.1  


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。。
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.
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