An anomaly detection algorithm for hyperspectral images using subspace sparse representation

被引：0
|
作者：
Cheng B. [1 ,2 ]
Zhao C. [3 ]
Zhang L. [2 ,3 ]
机构：
[1] College of Computer Science and Technology, Harbin Engineering University, Harbin
[2] College of Physics and Electricity Information Engineering, Daqing Normal University, Daqing
[3] College of Information and Communication, Harbin Engineering University, Harbin
来源：
Cheng, Baozhi (chengbaozhigy@163.com) | 1600年 / Editorial Board of Journal of Harbin Engineering卷 / 38期
关键词：
Anomaly target detection; Fuzzy clustering; Hyperspectral imagery; Particle swarm optimization; Sparse representation; Sparsity divergence index; Subspace;
D O I：
10.11990/jheu.201604006
中图分类号：
学科分类号：
摘要：
To overcome the low precision of hyperspectral imagery anomaly target detection caused by sparse representation, this paper proposes a new algorithm for anomaly target detection using subspace sparse representation. First, the algorithm optimizes fuzzy C-mean clustering using the particle swarm optimization method. Bands with similar features in the original hyperspectral image are placed in the same class, thereby dividing the whole hyperspectral image into a number of band subspaces but not changing its spatial and spectral features. Then, each subspace is detected by anomaly target detection using a spectral and spatial sparsity divergence index joint weighting. The final target detection result is obtained by overlaying the results of each subspace. Experiments were conducted using real AVIRIS data and the simulation results show that the proposed algorithm achieved very promising anomaly detection performance, with high precision and lower false alarm probability. © 2017, Editorial Department of Journal of HEU. All right reserved.
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页码：640 / 645
页数：5
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