A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection
	Wang Cai-ling 1,2; Wang Hong-wei 3; Hu Bing-liang1 ; Wen Jia4 ; Xu Jun 5; Li Xiang-juan 2
刊名	spectroscopy and spectral analysis
	2016-04-01
卷号	36 期号:4 页码:1163-1169
关键词	Target detection Spatial-spectral algorithm Hyperspectral image processing Neighborhood clustering Statistical operators
ISSN号	1000-0593
产权排序	1
英文摘要	with high-resolution spatial information and continuous spectrum information, hyperspectral remote sensing image has a unique advantage in the field of target detection. traditional hyperspectral remote sensing image target detection methods emphasis on using spectral information to determine deterministic algorithm and statistical algorithms. deterministic algorithms find the target by calculating the distance between the target spectrum and detected spectrum however, they are unable to detect sub-pixel target and are easily affected by noise. statistical methods which calculate background statistical characteristics to detect abnormal point as target. it can detect subpixel target targets and small targets better thanbig size target,. with the spatial resolution increasing, subpixel target detection target has gradually grown to a single pixel and multi-pixel target. at this point, hyperspectral image usually has large homogeneous regions where the neighboring pixels wihin the regions consist of the same type of materials and have a similar spectral characteristics, therefore, the spatial information should be needed to incorporate into the algorithm for targe detection. this paper proposes an algorithm for hyperspectral target detection combined spectrum characteristics and spatial characteristics. the algorithm is based on traditional target detection operator and combined neighborhood clustering statistics. firstly, the algorithm uses target detection operator to divided hyperspectral image into a potential target region and background region. then, it calculates the centroid of the potential target area. finally, as the centroid for neighborhood clustering center to dust data in order to exclud background from potential target area, through iterative calculation to obtain the final results of the target detection. the traditional statistics algorithms defines the total image as background area in order to extract background statistics features, and the algorithm propsed devided the total image into background part and potential target part, which cut off the target interference for background statistics feature extraction. compared with cem operators and ace operators, the algorithm proposed outperforms than traditional operators in big target detection.
WOS标题词	science & technology ; technology
类目[WOS]	spectroscopy
研究领域[WOS]	spectroscopy
收录类别	SCI ; EI
语种	中文
WOS记录号	WOS:000374625000047
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28128]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging, Xian 710119, Peoples R China 2.Xian Shiyou Univ, Sch Comp Sci, Xian 710065, Peoples R China 3.Chinese Peoples Armed Police Force, Engn Univ, Xian 710086, Peoples R China 4.Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China 5.East China Jiaotong Univ, Sch Informat Engn, Nanchang 330013, Peoples R China
推荐引用方式 GB/T 7714	Wang Cai-ling,Wang Hong-wei,Hu Bing-liang,et al. A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection[J]. spectroscopy and spectral analysis,2016,36(4):1163-1169.
APA	Wang Cai-ling,Wang Hong-wei,Hu Bing-liang,Wen Jia,Xu Jun,&Li Xiang-juan.(2016).A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection.spectroscopy and spectral analysis,36(4),1163-1169.
MLA	Wang Cai-ling,et al."A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection".spectroscopy and spectral analysis 36.4(2016):1163-1169.