Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM
文献类型:期刊论文
作者 | Wu, Yiquan; Zhou, Yang; Long, Yunlin |
刊名 | guangxue xuebao/acta optica sinica
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出版日期 | 2015-09-10 |
卷号 | 35期号:9 |
其他题名 | 基于自适应参数支持向量机的高光谱遥感图像小目标检测 |
英文摘要 | as for the problem of small target detection in hyperspectral remote sensing image, a detection method based on adaptiveparameter support vector machine (svm) is proposed. the low dimensional information of the hyperspectral image is obtained using the method of principal component analysis (pca) and the redundancy of data is reduced. then, small targets are positioned fast and roughly by an unsupervised detection method, and the posterior information of svm is got by the position result. the kernel parameter of svm is determined adaptively based on the posterior information and the criteria of divergence in the kernel space. the best hyperplane in the kernel space for the segmentation of targets and background is found by the svm. pixels are separated to targets and background by the best hyperplane. the accurate and stable target detection result is obtained by iteration. a large number of experimental results show that, compared to the existing methods such as rx method, kernel rx method and support vector data description (svdd) method, the proposed method is more effective to detect small targets accurately in the hyperspectral remote sensing image. ©, 2015, chinese optical society. all right reserved. |
收录类别 | EI |
语种 | 中文 |
公开日期 | 2015-12-08 |
源URL | [http://ir.opt.ac.cn/handle/181661/27323] ![]() |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
推荐引用方式 GB/T 7714 | Wu, Yiquan,Zhou, Yang,Long, Yunlin. Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM[J]. guangxue xuebao/acta optica sinica,2015,35(9). |
APA | Wu, Yiquan,Zhou, Yang,&Long, Yunlin.(2015).Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM.guangxue xuebao/acta optica sinica,35(9). |
MLA | Wu, Yiquan,et al."Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM".guangxue xuebao/acta optica sinica 35.9(2015). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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