Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

IEEE Geoscience and Remote Sensing Letters

Volumn 14, Issue 5, 2017, Pages 597-601

  Li, Wei ^a   Wu, Guodong ^a   Du, Qian ^b  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Anomaly detection; convolutional neural network (CNN); deep learning; hyperspectral imagery

Indexed keywords

DEEP LEARNING; NEURAL NETWORKS; SPECTROSCOPY;

ANOMALY DETECTION; ANOMALY DETECTION FRAMEWORKS; COLLABORATIVE REPRESENTATIONS; CONVOLUTIONAL NEURAL NETWORK; HYPER-SPECTRAL IMAGERIES; SIMILARITY MEASUREMENTS; SIMILARITY SCORES; SPARSE REPRESENTATION;

PIXELS;

EID: 85016510342     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2017.2657818     Document Type: Article

References (25)

1. L. Zhang, Q. Zhang, L. Zhang, D. Tao, X. Huang, and B. Du, "Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding," Pattern Recognit., vol. 48, no. 10, pp. 3102-3112, Oct. 2015.
2. B. Du and L. Zhang, "A discriminative metric learning based anomaly detection method," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6844-6857, Nov. 2014.
3. I. S. Reed and X. Yu, "Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution," IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 10, pp. 1760-1770, Oct. 1990.
4. J. M. Molero, E. M. Garzón, I. García, and A. Plaza, "Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 801-814, Apr. 2013.
5. C.-I. Chang and S.-S. Chiang, "Anomaly detection and classification for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1314-1325, Jun. 2002.
6. O. Duran and M. Petrou, "A time-efficient method for anomaly detection in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3894-3904, Dec. 2007.
7. L. Ma, M. M. Crawford, and J. Tian, "Local manifold learning-based k-nearest-neighbor for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4099-4109, Nov. 2010.
8. B. Du and L. Zhang, "Random-selection-based anomaly detector for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1578-1589, May 2011.
9. S. Khazai, A. Safari, B. Mojaradi, and S. Homayouni, "An approach for subpixel anomaly detection in hyperspectral images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 769-778, Apr. 2013.
10. W. Li and Q. Du, "Decision fusion for dual-window-based hyperspectral anomaly detector," J. Appl. Remote Sens., vol. 9, no. 1, p. 097297, Feb. 2015.
11. W. M. Liu and C. I. Chang, "Multiple-window anomaly detection for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 644-658, Apr. 2013.
12. W. Li and Q. Du, "Collaborative representation for hyperspectral anomaly detection," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1463-1474, Mar. 2015.
13. J. Li, H. Zhang, L. Zhang, and L. Ma, "Hyperspectral anomaly detection by the use of background joint sparse representation," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2523-2533, Jun. 2015.
14. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Simultaneous joint sparsity model for target detection in hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 4, pp. 676-680, Jul. 2011.
15. W. Li, Q. Du, and B. Zhang, "Combined sparse and collaborative representation for hyperspectral target detection," Pattern Recognit., vol. 48, no. 12, pp. 3904-3916, Dec. 2015.
16. Y. Zhang, B. Du, and L. Zhang, "A sparse representation-based binary hypothesis model for target detection in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1346-1354, Mar. 2015.
17. Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, "Deep learning-based classification of hyperspectral data," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2094-2107, Jun. 2014.
18. W. Li, G. Wu, F. Zhang, and Q. Du, "Hyperspectral image classification using deep pixel-pair features," IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 844-853, Feb. 2017.
19. L. Zhang, L. Zhang, and B. Du, "Deep learning for remote sensing data: A technical tutorial on the state of the art," IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 22-40, Jun. 2016.
20. Y. Chen, X. Zhao, and X. Jia, "Spectral-spatial classification of hyperspectral data based on deep belief network," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2381-2392, Jun. 2015.
21. X. Ma, H. Wang, and J. Geng, "Spectral-spatial classification of hyperspectral image based on deep auto-encoder," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 9, pp. 4073-4085, Sep. 2016.
22. W. Hu, Y. Huang, L. Wei, F. Zhang, and H. Li, "Deep convolutional neural networks for hyperspectral image classification," J. Sensors, vol. 2015, Jan. 2015, Art. no. 258619.
23. A. Romero, C. Gatta, and G. Camps-Valls, "Unsupervised deep feature extraction for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1349-1362, Mar. 2016.
24. W. Zhao and S. Du, "Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4544-4554, Aug. 2016.
25. J. A. Hanley and B. J. McNeil, "A method of comparing the areas under receiver operating characteristic curves derived from the same cases," Radiology, vol. 148, no. 3, pp. 839-843, Sep. 1983.