[1] Manolakis, D., Marden, D., and Shaw, G. A., “Hyperspectral Image Processing for Automatic Target Detection Applications”, Lincoln Lab. J., Vol. 14, No. 1, pp. 79–116, 2003.
[2] Ma, L., Crawford, M.M., and Tian, J., “Local Manifold Learning Based K-nearest-neighbor for Hyperspectral Image Classification”, IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 11, pp. 4099–4109, 2010.
[3] Sakla, W., Chan, A., Ji, J., and Sakla, A., “A SVDD-based Algorithm for Target Detection in Hyperspectral Imagery”, IEEE Geosci. Remote Sens. Lett., Vol. 8, No. 2, pp. 384–388, 2011.
[4] Imani, M., “Anomaly Detection from Hyperspectral Images, Using Clustering Based Feature Reduction”, Journal of the Indian Society of Remote Sensing, Vol. 46, No. 9, pp.1389–1397, 2018.
[5] Du, B. and Zhang, L., “A Discriminative Metric Learning Based Anomaly Detection Method”, IEEE Trans. Geosci. Remote Sens., Vol. 52, No. 11, pp. 6844–6857, 2014.
[6] Zhao, R., Du, B., and Zhang, L., “Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework”, IEEE Trans. Geosci. Remote Sens., Vol. 55, No. 6, pp. 3208-3222, 2017.
[7] Imani, M., “Attribute Profile Based Target Detection Using Collaborative and Sparse Representation”, Neurocomputing, Vol. 313, pp. 364–376, 2018.
[8] Reed, I.S. and Yu, X., “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.
[9] Guo, Q., Zhang, B., Ran, Q., Gao, L., Li, J., and Plaza, A., “Weighted- Rxd and Linear Filter-based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery”, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., Vol. 7, No. 6, pp. 2351–2366, 2014.
[10] Kwon, H. and Nasrabadi, N.M., “Kernel RX-algorithm: A Nonlinear Anomaly Detector for Hyperspectral Imagery”, IEEE Trans. Geosci. Remote Sens., Vol. 43, No. 2, pp. 388–397, 2005.
[11] Schaum, A.P., “Hyperspectral Anomaly Detection Beyond RX”, Proc. SPIE, Vol. 6565, Art., No. 656502, 2007.
[12] Li, W. and Du, Q., “Collaborative Representation for Hyperspectral Anomaly Detection”, IEEE Trans. Geosci. Remote Sens., Vol. 53, No. 3, pp. 1463–1474, 2015.
[13] Li, J., Zhang, H., Zhang, L., and Ma, L., “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, 2015.
[14] Xu, Y., Wu, Z., Li, J., Plaza, A., and Wei, Z., “Anomaly Detection in Hyperspectral Images Based on Low-rank and Sparse Representation”, IEEE Trans. Geosci. Remote Sens., Vol. 54, No. 4, pp. 1990–2000, 2016.
[15] Zhang, Y., Du, B., Zhang, L., and Wang, S., “A Low-rank and Sparse Matrix Decomposition-based Mahalanobis Distance Method for Hyperspectral Anomaly Detection”, IEEE Trans. Geosci. Remote Sens., Vol. 54, No. 3, pp. 1376–1389, 2016.
[16] Ning, M., Yu, P., Shaojun, W., and Wei, G., " A Weight Sae Based Hyperspectral Image Anomaly Targets Detection”, 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Yangzhou, pp. 511-515, 2017.
[17] Zhao, R., Du, B., Zhang, L., and Zhang, L., “Beyond Background Feature Extraction: an Anomaly Detection Algorithm Inspired by Slowly Varying Signal Analysis”, IEEE Trans. Geosci. Remote Sens., Vol. 54, No. 3, pp. 1757–1774, 2016.
[18] Du, B., Zhao, R., Zhang, L., and Zhang, L., “A Spectral-spatial Based Local Summation Anomaly Detection Method for Hyperspectral Images”, Signal Process., Vol. 124, pp. 115–131, 2016.
[19] Imani, M., “RX Anomaly Detector with Rectified Background”, IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 8, pp. 1313-1317, 2017.
[20] Imani, M., “Difference Based Target Detection Using Mahalanobis Distance and Spectral Angle”, International Journal of Remote Sensing, 2018.
[21] Kraut, S., Scharf, L.L., and McWhorter, L.T., “Adaptive Subspace Detectors”, IEEE Transactions on Signal Processing, Vol. 49, No. 1, pp. 1-16, 2001.
[22] Chen, Y., Nasrabadi, N.M., Tran, T.D., “Sparse Representation for Target Detection in Hyperspectral Imagery”, IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 3, pp. 629-640, 2011.
[23] Robey, F.C., Fuhrmann, D.R., Kelly, E.J., and Nitzberg, R., “A CFAR Adaptive Matched Filter Detector”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 28, No. 1, pp. 208-216, 1992.
[24] Scharf, L.L. and Friedlander, B., “Matched Subspace Detectors”, IEEE Transactions on Signal Processing, Vol. 42, No. 8, pp. 2146-2157, 1994.
[25] Harsanyi, J.C. and Chang, C.I., “Hyperspectral Image Classification and Dimensionality Reduction: an Orthogonal Subspace Projection Approach”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 4, pp. 779-785, 1994.
[26] Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., and Goetz, A.F.H., “The Spectral Image Processing System (SIPS) Interactive Visualization and Analysis of Imaging Spectrometer Data”, Remote Sensing of Environment, Vol. 44, No. 2, pp. 145-163, 1993.
[27] Camps-Valls, G., “Kernel Spectral Angle Mapper”, Electronics Letters, Vol. 52, No.14, pp. 1218-1220, 2016.