| Detection of hyperspectral small targets based on projection pursuit optimized by bee colony | |
| 其他题名 | 基于蜂群优化投影寻踪的高光谱小目标检测 |
| Wu, Yiquan1,2; Zhou, Yang1; Long, Yunlin1; Wu, Yiquan (nuaaimage@163.com) | |
| 作者部门 | 光谱成像技术实验室 |
| 2016-06-01 | |
| 发表期刊 | Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
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| ISSN | 02543087 |
| 卷号 | 37期号:6页码:1347-1355 |
| 产权排序 | 2 |
| 摘要 | In order to further improve the operation speed and reduce the false alarm rate of the unsupervised detection method for small targets in hyperspectral remote sensing images, a detection method based on the projection pursuit (PP) optimized by improved artificial bee colony (ABC) optimization algorithm and K;nearest neighbor (KNN) is proposed in this paper. Firstly, the kernel principal component analysis (KPCA) method is adopted to perform the dimension reduction of the original hyperspectral remote sensing images. Then, the method jointly defining the kurtosis and skewness according to the neighborhood pixels is proposed, the combination of the kurtosis and skewness is taken as the projection index. The improved artificial bee colony algorithm is taken as the optimization algorithm. The projection pursuit is used to obtain the projection images layer by layer from the low dimensional hyperspectral remote sensing images, and the small targets are extracted according to the histogram of these projection images. Finally, the linear discriminant analysis (LDA) is used to extract the features of the pixels, and the weighted K;nearest neighbor method is used to purify the preliminary detection results of the small targets. A large number of experiment results show that compared with the RX method, independent component analysis (ICA) method and the projection pursuit method based on chaotic particle swarm optimization (CPSO), the proposed method not only can detect the small targets in hyperspectral remote sensing image accurately, but also has faster operation speed. © 2016, Science Press. All right reserved. |
| 关键词 | Algorithms Discriminant Analysis Evolutionary Algorithms Higher Order Statistics Image Analysis Image Processing Image Reconstruction Independent Component Analysis Motion Compensation Nearest Neighbor Search Optimization Particle Swarm Optimization (Pso) Pixels Principal Component Analysis Remote Sensing Spectroscopy Statistical Methods |
| 收录类别 | EI |
| 语种 | 中文 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28240 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | Wu, Yiquan (nuaaimage@163.com) |
| 作者单位 | 1.College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing; 211106, China 2.Key Laboratory of Spectral Imaging Technology, CAS, Xi'an Institute of Optics and Precision Mechanics, Xi'an; 710119, China |
| 推荐引用方式 GB/T 7714 | Wu, Yiquan,Zhou, Yang,Long, Yunlin,et al. Detection of hyperspectral small targets based on projection pursuit optimized by bee colony[J]. Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument,2016,37(6):1347-1355. |
| APA | Wu, Yiquan,Zhou, Yang,Long, Yunlin,&Wu, Yiquan .(2016).Detection of hyperspectral small targets based on projection pursuit optimized by bee colony.Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument,37(6),1347-1355. |
| MLA | Wu, Yiquan,et al."Detection of hyperspectral small targets based on projection pursuit optimized by bee colony".Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 37.6(2016):1347-1355. |
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| Detection of hypersp（1905KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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