Abstract

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

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  1. J. Yuan, Y. J. Zhang, and F. P. GaoA overview on linear hyperspectral unmixingJ. Infrared. Millimeter. Waves201837553571
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  11. W. M. Liu and C. I. ChangMultiple-window anomaly detection for hyperspectral imageryIEEE. J. Sel. Top. Appl. Earth. Obs. Remote. Sens.20136644658
  12. Y. Chen, N. M. Naarabadi, and T. D. TranHyperspectral image classification using dictionary-based sparse representationIEEE Trans. Geosci. Remote Sens.20114939733985
  13. Z. Yuan, H. Sun, K. Ji, Z. Li, and H. ZouLocal sparsity divergence for hyperspectral anomaly detectionIEEE Geosci. Remote Sens. Lett.20141116971701

Other (13)

J. Yuan, Y. J. Zhang, and F. P. GaoA overview on linear hyperspectral unmixingJ. Infrared. Millimeter. Waves201837553571

Y. F. Qi and Z. Y. MaHyperspectral image classification method based on cooperative representation of neighborhood spectral probabilitiesLaser. Technol.2019431216

L. X. ShanHyperspectral image sub-pixel small target detection, M. S. ThesisXidian UniversityShaanxi2017

P. BajorskiTarget detection under misspecified models in hyperspectral imagesIEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.20125470477

L. Sun, J. H. Bao, and Y. C. LiuAnalysis of target detection algorithm for hyperspectral imagesSci. Surv. Mapp.201237131132+108

I. S. Reed and X. YuAdaptive multiple-band CFAR detection of an optical pattern with unknown spectral distributionIEEE Trans. Acoust. Speech. Signal. Process19903817601770

X. L. Yu, L. E. Hoff, I. S. Reed, A. M. Chen, and L. B. StottsAutomatic target detection and recognition in multiband imagery: A unified ML detection and estimation approachIEEE Trans. Image Process19976143156

S. S. Du, S. Y. Li, and Z. Y. ZengInfluence of background uncertainty on hyperspectral anomaly target detectionJ. PLA Univ. Sci. Technol. (Natural Science Edition)201617598604

T. E. Smetek and K. W. BauerFinding hyperspectral anomalies using multivariate outlier detectionProc. IEEE Aerospace ConferenceBig Sky, MTUSA2007March

Y. P. Taitano, B. A. Geier, and K. W. BauerA locally adaptable iterative RX detectorEURASIP. J. Adv. Signal. Process.20102010341908

W. M. Liu and C. I. ChangMultiple-window anomaly detection for hyperspectral imageryIEEE. J. Sel. Top. Appl. Earth. Obs. Remote. Sens.20136644658

Y. Chen, N. M. Naarabadi, and T. D. TranHyperspectral image classification using dictionary-based sparse representationIEEE Trans. Geosci. Remote Sens.20114939733985

Z. Yuan, H. Sun, K. Ji, Z. Li, and H. ZouLocal sparsity divergence for hyperspectral anomaly detectionIEEE Geosci. Remote Sens. Lett.20141116971701

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