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张乐飞-- Hyperspectral Remote Sensing Image Subpixel Target Detection Based on....

发布日期:2014-06-29 11:20:09 阅读次数:[3542]次 作者:

核心提示:张乐飞-- Hyperspectral Remote Sensing Image Subpixel Target Detection Based on....

标题:Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning
作者:Zhang, LF (Zhang, Lefei); Zhang, LP (Zhang, Liangpei); Tao, DC (Tao, Dacheng); Huang, X (Huang, Xin); Du, B (Du, Bo)
来源出版物:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷:52 期:8 页:4955-4965 DOI:10.1109/TGRS.2013.2286195 出版年:AUG 2014
Web of Science 核心合集中的 "被引频次":1
被引频次合计:1
摘要:The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection.
地址:[Zhang, Lefei; Du, Bo] Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China.
[Zhang, Lefei; Huang, Xin] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
[Tao, Dacheng] Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia.
通讯作者地址:Zhang, LF (通讯作者),Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China.
电子邮件地址:zhanglefei@whu.edu.cn; zlp62@whu.edu.cn; dacheng.tao@uts.edu.au; huang_whu@163.com

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