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European Journal of Remote Sensing Volume 50, 2017 - Issue 1
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Original Articles

Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation

Lili ZhangDepartment of Signal and Information Processing, Harbin Engineering University, College of Information and Communication Engineering, Harbin, China;Department of Electronic Information Engineering, Daqing Normal University, College of Mechanical and Electrical Engineering, Daqing, ChinaView further author information
&
Chunhui ZhaoDepartment of Signal and Information Processing, Harbin Engineering University, College of Information and Communication Engineering, Harbin, ChinaCorrespondence[email protected]
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Pages 362-376 | Received 24 Jun 2016, Accepted 15 May 2017, Published online: 13 Jun 2017

References

  • Bachmann, C.M., Ainsworth, T.L., & Fusina, R.A. (2005). Exploiting manifold geometry in hyperspectral imagery. IEEE Transactions on Geoscience & Remote Sensing, 43(3), 441–376. doi:10.1109/TGRS.2004.842292
  • Bajorski, P. (2012). Target detection under misspecified models in hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 5(2), 470–477. doi:10.1109/JSTARS.2012.2188095
  • Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373–1396. doi:10.1162/089976603321780317
  • Borghys, D., Kåsen, I., Achard, V., & Perneel, C. (2012a). Comparative evaluation of hyperspectral anomaly detectors in different types of background. Proceedings of SPIE – The International Society for Optical Engineering, 8390(12), 83902J-83902J-12. doi:10.1117/12.920387
  • Borghys, D., Kåsen, I., Achard, V., & Perneel, C. (2012b). Hyperspectral anomaly detection: Comparative evaluation in scenes with diverse complexity. Journal of Electrical & Computer Engineering, 2012, 1–16. doi:10.1155/2012/162106
  • Camps-Valls, G., & Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience & Remote Sensing, 43(6), 1351–1362. doi:10.1109/TGRS.2005.846154
  • Chen, Y., Nasrabadi, N.M., & Tran, T.D. (2011). Sparse representation for target detection in hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5(3), 629–640. doi:10.1109/JSTSP.2011.2113170
  • He, X. (2005). Locality preserving projections. Advances in Neural Information Processing Systems, 45(1), 186–197.
  • He, X., Cai, D., Yan, S., & Zhang, H.J. (2005). Neighborhood preserving embedding. Tenth IEEE International Conference on Computer Vision, 2, 1208–1213. doi:10.1109/ICCV.2005.167
  • Hinton, G., & Roweis, S. (2010). Stochastic neighbor embedding. Advances in Neural Information Processing Systems, 41(4), 833–840.
  • Kwon, H., & Nasrabadi, N.M. (2005). Kernel RX-algorithm: A nonlinear anomaly detector for hyper-spectral imagery. IEEE Transactions on Geoscience & Remote Sensing, 43(2), 388–397. doi:10.1109/TGRS.2004.841487
  • Kwon, H., & Nasrabadi, N.M. (2007). A comparative analysis of kernel subspace target detectors for hyperspectral imagery. Journal on Advances in Signal Processing, 1, 1–13. doi:10.1155/2007/29250
  • Li, J., Zhang, H., Zhang, L., & Ma, L. (2015). Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8(6), 2523–2533. doi:10.1109/JSTARS.2015.2437073
  • Li, W., & Du, Q. (2015). Collaborative representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience & Remote Sensing, 53(3), 1463–1474. doi:10.1109/TGRS.2014.2343955
  • Lou, C., & Zhao, H. (2015). Local density-based anomaly detection in hyperspectral image. Journal of Applied Remote Sensing, 9(1), 095070. doi:10.1117/1.JRS.9.095070
  • Ma, L., Crawford, M.M., & Tian, J. (2010). Anomaly detection for hyperspectral images based on robust locally linear embedding. Journal of Infrared Millimeter & Terahertz Waves, 31(6), 753–762. doi:10.1007/s10762-010-9630-3
  • Manolakis, D., & Shaw, G. (2002). Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine, 19(1), 29–43. doi:10.1109/79.974724
  • Matteoli, S., Diani, M., & Corsini, G. (2010). A tutorial overview of anomaly detection in hyperspectral images. IEEE Aerospace & Electronic Systems Magazine, 25(7), 5–28. doi:10.1109/MAES.2010.5546306
  • Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., … Trianni, G. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(9), S110–S122. doi:10.1016/j.rse.2007.07.028
  • Plaza, A., Martin, G., Plaza, J., Zortea, M., & Sanchez, S. (2011). Recent developments in endmember extraction and spectral unmixing. Optical Remote Sensing, 3, 235–267. doi:10.1007/978-3-642-14212-3_12
  • Reed, I.S., & Yu, X. (1990). Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics Speech & Signal Processing, 38(10), 1760–1770. doi:10.1109/29.60107
  • Roweis, S.T., & Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. doi:10.1126/science.290.5500.2323
  • Shaw, G., & Manolakis, D. (2002). Signal processing for hyperspectral image exploitation. IEEE Signal Processing Magazine, 19(1), 12–16. doi:10.1109/79.974715
  • Stein, D.W.J., Beaven, S.G., Hoff, L.E., Winter, E.M., Schaum, A.P., & Stocker, A.D. (2002). Anomaly detection from hyperspectral imagery. IEEE Signalling Processing Magazine, 19(1), 58–69. doi:10.1109/79.974730
  • Tenenbaum, J.B., Silva, V., & Langford, J.C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. doi:10.1126/science.290.5500.2319
  • Tropp, J.A., Gilbert, A.C., & Strauss, M.J. (2006). Algorithms for simultaneous sparse approximation. part i: Greedy pursui. Signal Processing, 86(3), 572–588. doi:10.1016/j.sigpro.2005.05.030
  • Wang, J. (2008). Improve local tangent space alignment using various dimensional local coordinates. Neurocomputing, 71(16–18), 3575–3581. doi:10.1016/j.neucom.2008.02.008
  • Yuan, Z., Sun, H., Ji, K., & Li, Z. (2014). Local sparsity divergence for hyperspectral anomaly detection. IEEE Geoscience & Remote Sensing Letters, 11(10), 1697–1701. doi:10.1109/LGRS.2014.2306209
  • Zhang, L., & Zhao, C. (2016). Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection. Journal of Applied Remote Sensing, 10(2), 025026. doi:10.1117/1.JRS.10.025026
  • Zhang, T., Yang, J., Zhao, D., & Ge, X. (2007). Linear local tangent space alignment and application to face recognition. Neurocomputing, 70(7–9), 1547–1553. doi:10.1016/j.neucom.2006.11.007
  • Zhang, Y., Du, B., Zhang, L., & Wang, S. (2016). A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience & Remote Sensing, 54(3), 1376–1389. doi:10.1109/TGRS.2015.2479299
  • Zhang, Z.Y., & Zha, H.Y. (2004). Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Siam Journal on Scientific Computing, 26(1), 313–338. doi:10.1137/S1064827502419154
  • Zhao, R., Du, B., & Zhang, L.P. (2014). A robust nonlinear hyperspectral anomaly detection approach. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 7(4), 1227–1234. doi:10.1109/JSTARS.2014.2311995

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