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：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2017年 / 38卷 / 04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.

引用

页码：640 / 645

页数：5

共 23 条

[11]

Zhao C., Li X., Zhu H., Hyperspectral imaging target detection algorithm based on spatial 4 neighborhood for sparse representation, Journal of Harbin Engineering University, 34, 9, pp. 1-8, (2013)

[12]

Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, (1981)

[13]

Pal N.R., Pal K., Keller J.M., Et al., A possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems, 13, 4, pp. 517-530, (2005)

[14]

Chen X., Li C., Li F., Et al., Image segmentati- on based on pso and fuzzy C-means clustering algorithm, Computer engineering and applications, 44, 18, pp. 181-182, (2008)

[15]

Kennedy J., Eberhart R.C., Particle swarm optimization, IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)

[16]

Paoli M., Melgani F., Clustering of hyperspectral image based on multiobjective particle swarm optimization, IEEE Transactions on Geoscience and Remote Sensing, 47, 12, pp. 4175-4178, (2009)

[17]

Taher N., Babak A., An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis, Applied Soft Computing, 10, 1, pp. 183-197, (2010)

[18]

Lv Y., Lin J., Parallel PSO combined with K-means clustering algorithm based on MPI, Journal of Computer Applications, 31, 2, pp. 428-431, (2011)

[19]

Song X., Jiao L., Classification of hype-rspectral remote sensing image based on sparse represe-ntation and spectral Information, Journal of Electronics & Information Technology, 34, 2, pp. 268-272, (2012)

[20]

Gao G., A parzen-window-kernel-based cfar algorithm for ship detection in sar images, IEEE Geoscience and Remote Sensing Letters, 8, 3, pp. 557-561, (2011)

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