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Proceedings Paper

Sparse representation based multi-threshold segmentation for hyperspectral target detection
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Paper Abstract

A sparse representation based multi-threshold segmentation (SRMTS) algorithm for target detection in hyperspectral images is proposed. Benefiting from the sparse representation, the high-dimensional spectral data can be characterized into a series of sparse feature vectors which has only a few nonzero coefficients. Through setting an appropriate threshold, the noise removed sparse spectral vectors are divided into two subspaces in the sparse domain consistent with the sample spectrum to separate the target from the background. Then a correlation and a vector 1-norm are calculated respectively in the subspaces. The sparse characteristic of the target is used to ext ract the target with a multi -threshold method. Unlike the conventional hyperspectral dimensionality reduction methods used in target detection algorithms, like Principal Components Analysis (PCA) and Maximum Noise Fraction (MNF), this algorithm maintains the spectral characteristics while removing the noise due to the sparse representation. In the experiments, an orthogonal wavelet sparse base is used to sparse the spectral information and a best contraction threshold to remove the hyperspectral image noise according to the noise estimation of the test images. Compared with co mmon algorithms, such as Adaptive Cosine Estimator (ACE), Constrained Energy Minimizat ion (CEM) and the noise removed MNF-CEM algorithm, the proposed algorithm demonstrates higher detection rates and robustness via the ROC curves.

Paper Details

Date Published: 30 August 2013
PDF: 13 pages
Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 89100M (30 August 2013); doi: 10.1117/12.2032686
Show Author Affiliations
Wei-yi Feng, Nanjing Univ. of Science and Technology (China)
Science and Technology on Low-Light-Level Night Vision Lab. (China)
Qian Chen, Nanjing Univ. of Science and Technology (China)
Zhuang Miao, Science and Technology on Low-Light-Level Night Vision Lab. (China)
Wei-ji He, Nanjing Univ. of Science and Technology (China)
Guo-hua Gu, Nanjing Univ. of Science and Technology (China)
Jia-yan Zhuang, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8910:
International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
Lifu Zhang; Jianfeng Yang, Editor(s)

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