Sparse representation based multi-threshold segmentation for hyperspectral target detection

Wei-yi Feng; Qian Chen; Zhuang Miao; Wei-ji He; Guo-hua Gu; Jia-yan Zhuang

doi:10.1117/12.2032686

30 August 2013 Sparse representation based multi-threshold segmentation for hyperspectral target detection

Wei-yi Feng, Qian Chen, Zhuang Miao, Wei-ji He, Guo-hua Gu, Jia-yan Zhuang

Proceedings Volume 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications; 89100M (2013) https://doi.org/10.1117/12.2032686
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

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.

Citation Download Citation

Wei-yi Feng, Qian Chen, Zhuang Miao, Wei-ji He, Guo-hua Gu, and Jia-yan Zhuang "Sparse representation based multi-threshold segmentation for hyperspectral target detection", Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 89100M (30 August 2013); https://doi.org/10.1117/12.2032686

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