Hyperspectral anomaly detection: a performance comparison of existing techniques

References

• Anowar, F., S. Sadaoui, and B. Selim. 2021. “Conceptual and Empirical Comparison of Dimensionality Reduction Algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE).” Computer Science Review 40: 100378. doi:10.1016/j.cosrev.2021.100378.
   Web of Science ®Google Scholar
• Arisoy, S., N. M. Nasrabadi, and K. Kayabol. 2021. “GAN-Based Hyperspectral Anomaly Detection.” 2020 28th European Signal Processing Conference (EUSIPCO), 1891–1895.
   Google Scholar
• Atkinson, P. M., and A. R. L. Tatnall. 1997. “Introduction Neural Networks in Remote Sensing.” International Journal of Remote Sensing 18 (4): 699–709.
   Web of Science ®Google Scholar
• Ayhan, B., M. Dao, C. Kwan, H.-M. Chen, J. F. Bell, and R. Kidd. 2017. “A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover with Applications to Image Fusion, Pixel Clustering, and Anomaly Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (10): 4553–4564.
   Web of Science ®Google Scholar
• Balasubramanian, M., and E. L. Schwartz. 2002. “The Isomap Algorithm and Topological Stability.” Science 295 (5552): 7. doi:10.1126/science.295.5552.7a.
   PubMed Web of Science ®Google Scholar
• Banerjee, A., P. Burlina, and C. Diehl. 2006. “A Support Vector Method for Anomaly Detection in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 44 (8): 2282–2291.
   Web of Science ®Google Scholar
• Bati, E., A. Çalışkan, A. Koz, and A. A. Alatan. 2015. “Hyperspectral Anomaly Detection Method Based on Auto-Encoder.” Image and Signal Processing for Remote Sensing XXI 9643: 96430N. doi:10.1117/12.2195180.
   Google Scholar
• Baur, C., B. Wiestler, S. Albarqouni, and N. Navab. 2018. “Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images.” International MICCAI Brainlesion Workshop, 161–169. Part of the Lecture Notes in Computer Science book series (LNIP, volume 11383).
   Google Scholar
• Bioucas-Dias, J. M., A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot. 2012. “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (2): 354–379.
   Web of Science ®Google Scholar
• Borghys, D., I. Kåsen, V. Achard, and C. Perneel. 2012. “Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity.” Journal of Electrical and Computer Engineering 2012: 1–16.
   Google Scholar
• Bradley, A. P. 1997. “The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms.” Pattern Recognition 30 (7): 1145–1159.
   Web of Science ®Google Scholar
• Camps-Valls, G., and L. Bruzzone. 2005. “Kernel-Based Methods for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 43 (6): 1351–1362.
   Web of Science ®Google Scholar
• Candès, E. J., X. Li, Y. Ma, and J. Wright. 2011. “Robust Principal Component Analysis?” Journal of the ACM (JACM) 58 (3): 1–37.
   Web of Science ®Google Scholar
• Carlotto, M. J. 2005. “A Cluster-Based Approach for Detecting Man-Made Objects and Changes in Imagery.” IEEE Transactions on Geoscience and Remote Sensing 43 (2): 374–387.
   Web of Science ®Google Scholar
• Chalapathy, R., and S. Chawla. 2019. “Deep Learning for Anomaly Detection: A Survey.” ArXiv Preprint ArXiv:1901.03407.
   Google Scholar
• Chang, C. I. 2021. “An Effective Evaluation Tool for Hyperspectral Target Detection: 3d Receiver Operating Characteristic Curve Analysis.” IEEE Transactions on Geoscience and Remote Sensing 59 (6): 5131–5153. doi:10.1109/TGRS.2020.3021671.
   Web of Science ®Google Scholar
• Chang, C. I., H. Cao, S. Chen, X. Shang, C. Yu, and M. Song. 2021. “Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 59 (3): 2403–2429. doi:10.1109/TGRS.2020.3002724.
   Web of Science ®Google Scholar
• Chang, C. I., H. Cao, and M. Song. 2021. “Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 4915–4932. doi:10.1109/JSTARS.2021.3068983.
   Web of Science ®Google Scholar
• Chen, S.-Y., C. Lin, C.-H. Tai, and S.-J. Chuang. 2018. “Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves.” Remote Sensing 10 (1): 96.
   Web of Science ®Google Scholar
• Chen, Y., Z. Lin, X. Zhao, G. Wang, and Y. Gu. 2014. “Deep Learning-Based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107.
   Web of Science ®Google Scholar
• Comon, P. 1994. “Independent Component Analysis, a New Concept?” Signal Processing 36 (3): 287–314.
   Web of Science ®Google Scholar
• Datta, A., S. Ghosh, and A. Ghosh. 2018. “PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images.” Advances in Principal Component Analysis: Research and Development, 19–46. doi:10.1007/978-981-10-6704-4_2.
   Google Scholar
• Du, Q., I. Kopriva, and H. H. Szu. 2006. “Independent-Component Analysis for Hyperspectral Remote Sensing Imagery Classification.” Optical Engineering 45 (1): 017008.
   Web of Science ®Google Scholar
• Du, B., and L. Zhang. 2011. “Random-Selection-Based Anomaly Detector for Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 49 (5): 1578–1589.
   Web of Science ®Google Scholar
• Du, B., R. Zhao, L. Zhang, and L. Zhang. 2016. “A Spectral-Spatial Based Local Summation Anomaly Detection Method for Hyperspectral Images.” Signal Processing 124: 115–131. doi:10.1016/j.sigpro.2015.09.037.
   Web of Science ®Google Scholar
• Faraggi, D., and B. Reiser. 2002. “Estimation of the Area under the ROC Curve.” Statistics in Medicine 21 (20): 3093–3106.
   PubMed Web of Science ®Google Scholar
• Fauvel, M., Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton. 2013. “Advances in Spectral-Spatial Classification of Hyperspectral Images.” Proceedings of the IEEE 101 (3): 652–675.
   Web of Science ®Google Scholar
• Fawcett, T. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–874.
   Web of Science ®Google Scholar
• Feng, S., S. Tang, C. Zhao, and Y. Cui. 2022. “A Hyperspectral Anomaly Detection Method Based on Low-Rank and Sparse Decomposition with Density Peak Guided Collaborative Representation.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–13. doi:10.1109/TGRS.2021.3054736.
   Web of Science ®Google Scholar
• Fu, X., S. Jia, L. Zhuang, M. Xu, J. Zhou, and Q. Li. 2021. “Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization.” IEEE Transactions on Geoscience and Remote Sensing 59 (11): 9553–9568. doi:10.1109/TGRS.2021.3049224.
   Web of Science ®Google Scholar
• Gao, C., H. Sun, T. Wang, M. Tang, N. I. Bohnen, M. L. T. M. Müller, T. Herman, et al. 2018. “Model-Based and Model-Free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease.” Scientific Reports 8 (1): 1–21.
   PubMedGoogle Scholar
• George, D., and P. Mallery. 2020. “Multidimensional Scaling.” IBM SPSS Statistics 26 Step by Step, 247–257. doi:10.4324/9780429056765-20.
   Google Scholar
• Goetz, A. F. H., G. Vane, J. E. Solomon, and B. N. Rock. 1985. “Imaging Spectrometry for Earth Remote Sensing.” Science 228 (4704): 1147–1153.
   PubMed Web of Science ®Google Scholar
• Golub, G. H., and C. Reinsch. 1971. “Singular Value Decomposition and Least Squares Solutions.” In Linear Algebra. Handbook for Automatic Computation. Vol. 2, edited by F. L. Bauer, 134–151. Berlin: Springer.
   Google Scholar
• Guo, Q., B. Zhang, Q. Ran, L. Gao, J. Li, and A. Plaza. 2014. “Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2351–2366.
   Web of Science ®Google Scholar
• Hannachi, A., and A. G. Turner. 2013. “Isomap Nonlinear Dimensionality Reduction and Bimodality of Asian Monsoon Convection.” Geophysical Research Letters 40 (8): 1653–1658. doi:10.1002/grl.50351.
   Web of Science ®Google Scholar
• Hansen, P. C. 1990. “Truncated Singular Value Decomposition Solutions to Discrete Ill-Posed Problems with Ill-Determined Numerical Rank.” SIAM Journal on Scientific and Statistical Computing 11 (3): 503–518.
   Web of Science ®Google Scholar
• Harsanyi, J. C., and C.-I. Chang. 1994. “Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach.” IEEE Transactions on Geoscience and Remote Sensing 32 (4): 779–785.
   Web of Science ®Google Scholar
• Hoffmann, H. 2007. “Kernel PCA for Novelty Detection.” Pattern Recognition 40 (3): 863–874.
   Web of Science ®Google Scholar
• Huang, Z., L. Fang, and S. Li. 2020. “Subpixel-Pixel-Superpixel Guided Fusion for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 58 (9): 5998–6007. doi:10.1109/TGRS.2019.2961703.
   Web of Science ®Google Scholar
• Huang, Q., W. Zhu, Y. Zhao, L. Chen, Y. Wang, T. Yue, and X. Cao. 2018, June. “Multispectral Image Intrinsic Decomposition via Subspace Constraint.” IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 6430–6439.
   Google Scholar
• Hussain, M., S. K. Wajid, A. Elzaart, and M. Berbar. 2011. “A Comparison of SVM Kernel Functions for Breast Cancer Detection.” 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, 145–150.
   Google Scholar
• Huyan, N., X. Zhang, H. Zhou, and L. Jiao. 2019. “Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction.” IEEE Transactions on Geoscience and Remote Sensing 57 (4): 2263–2276. doi:10.1109/TGRS.2018.2872590.
   Web of Science ®Google Scholar
• Hytla, P. C., R. C. Hardie, M. T. Eismann, and J. Meola. 2009. “Anomaly Detection in Hyperspectral Imagery: Comparison of Methods Using Diurnal and Seasonal Data.” Journal of Applied Remote Sensing 3 (1): 033546.
   Google Scholar
• Hyvarinen, A. 1999. “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis.” IEEE Transactions on Neural Networks 10 (3): 626–634.
   PubMed Web of Science ®Google Scholar
• Hyvärinen, A., and E. Oja. 2000. “Independent Component Analysis: Algorithms and Applications.” Neural Networks 13 (4–5): 411–430.
   PubMed Web of Science ®Google Scholar
• Kang, X., X. Zhang, S. Li, K. Li, J. Li, and J. A. Benediktsson. 2017. “Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters.” IEEE Transactions on Geoscience and Remote Sensing 55 (10): 5600–5611.
   Web of Science ®Google Scholar
• Kåsen, I., A. Rødningsby, T. V. Haavardsholm, and T. Skauli. 2008. “Band Selection for Hyperspectral Target Detection Based on a Multinormal Mixture Anomaly Detection Algorithm.” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 6966, 696606.
   Google Scholar
• Keerthi, S. S., and C.-J. Lin. 2003. “Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel.” Neural Computation 15 (7): 1667–1689.
   PubMed Web of Science ®Google Scholar
• Khan, M. J., H. S. Khan, A. Yousaf, K. Khurshid, and A. Abbas. 2018. “Modern Trends in Hyperspectral Image Analysis: A Review.” IEEE Access 6: 14118–14129.
   Web of Science ®Google Scholar
• Khazai, S., A. Safari, B. Mojaradi, and S. Homayouni. 2013. “An Approach for Subpixel Anomaly Detection in Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2): 769–778. doi:10.1109/JSTARS.2012.2210277.
   Web of Science ®Google Scholar
• Kiran, B. R., D. M. Thomas, and R. Parakkal. 2018. “An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos.” Journal of Imaging 4 (2): 36.
   Web of Science ®Google Scholar
• Klema, V., and A. Laub. 1980. “The Singular Value Decomposition: Its Computation and Some Applications.” IEEE Transactions on Automatic Control 25 (2): 164–176.
   Web of Science ®Google Scholar
• Kolda, T. G., and B. W. Bader. 2009. “Tensor Decompositions and Applications.” SIAM Review 51 (3): 455–500.
   Web of Science ®Google Scholar
• Küçük, S., and S. E. Yüksel. 2015. “Comparison of RX-Based Anomaly Detectors on Synthetic and Real Hyperspectral Data.” 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1–4.
   Google Scholar
• Kwon, H., and N. M. Nasrabadi. 2005. “Kernel RX-Algorithm: A Nonlinear Anomaly Detector for Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 43 (2): 388–397.
   Web of Science ®Google Scholar
• Lavanya, A., and S. Sanjeevi. 2013. “An Improved Band Selection Technique for Hyperspectral Data Using Factor Analysis.” Journal of the Indian Society of Remote Sensing 41 (2): 199–211.
   Web of Science ®Google Scholar
• Li, W., and Q. Du. 2013. “Unsupervised Nearest Regularized Subspace for Anomaly Detection in Hyperspectral Imagery.” 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 1055–1058.
   Google Scholar
• Li, W., and Q. Du. 2015. “Collaborative Representation for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 53 (3): 1463–1474.
   Web of Science ®Google Scholar
• Li, Shuangjiang, W. Wang, H. Qi, B. Ayhan, C. Kwan, and S. Vance. 2015. “Low-Rank Tensor Decomposition Based Anomaly Detection for Hyperspectral Imagery.” Proceedings – International Conference on Image Processing, ICIP, 2015-December, 4525–4529. doi:10.1109/ICIP.2015.7351663.
   Google Scholar
• Li, W., G. Wu, and Q. Du. 2017. “Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery.” IEEE Geoscience and Remote Sensing Letters 14 (5): 597–601.
   Web of Science ®Google Scholar
• Li, Shutao, K. Zhang, P. Duan, and X. Kang. 2019. “Hyperspectral Anomaly Detection with Kernel Isolation Forest.” IEEE Transactions on Geoscience and Remote Sensing 58 (1): 319–329.
   Web of Science ®Google Scholar
• Li, Shutao, K. Zhang, Q. Hao, P. Duan, and X. Kang. 2018. “Hyperspectral Anomaly Detection with Multiscale Attribute and Edge-Preserving Filters.” IEEE Geoscience and Remote Sensing Letters 15 (10): 1605–1609.
   Web of Science ®Google Scholar
• Li, J., H. Zhang, L. Zhang, and L. Ma. 2015. “Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 2523–2533. doi:10.1109/JSTARS.2015.2437073.
   Web of Science ®Google Scholar
• Lin, H.-T., and C.-J. Lin. 2003. “A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-Type Methods.” Neural Computation 3 (1–32): 16.
   Google Scholar
• Liu, W., and C.-I. Chang. 2004. “A Nested Spatial Window-Based Approach to Target Detection for Hyperspectral Imagery.” IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, 1.
   Google Scholar
• Liu, W.-M., and C.-I. Chang. 2013. “Multiple-Window Anomaly Detection for Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2): 644–658.
   Web of Science ®Google Scholar
• Liu, D., and L. Han. 2017. “Spectral Curve Shape Matching Using Derivatives in Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 14 (4): 504–508.
   Web of Science ®Google Scholar
• Liu, J., Z. Hou, W. Li, R. Tao, D. Orlando, and H. Li. 2022. “Multipixel Anomaly Detection with Unknown Patterns for Hyperspectral Imagery.” IEEE Transactions on Neural Networks and Learning Systems 33: 5557–5567. doi:10.1109/TNNLS.2021.3071026.
   PubMed Web of Science ®Google Scholar
• Ma, N., Y. Peng, S. Wang, and P. H. W. Leong. 2018. “An Unsupervised Deep Hyperspectral Anomaly Detector.” Sensors 18 (3): 693.
   PubMed Web of Science ®Google Scholar
• Maji, S., A. C. Berg, and J. Malik. 2008. “Classification Using Intersection Kernel Support Vector Machines Is Efficient.” 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8.
   Google Scholar
• Makki, I., R. Younes, C. Francis, T. Bianchi, and M. Zucchetti. 2017. “A Survey of Landmine Detection Using Hyperspectral Imaging.” ISPRS Journal of Photogrammetry and Remote Sensing 124: 40–53.
   Web of Science ®Google Scholar
• Manolakis, D. 2005. “Taxonomy of Detection Algorithms for Hyperspectral Imaging Applications.” Optical Engineering 44 (6): 066403. doi:10.1117/1.1930927.
   Web of Science ®Google Scholar
• Matteoli, S., M. Diani, and G. Corsini. 2010. “A Tutorial Overview of Anomaly Detection in Hyperspectral Images.” IEEE Aerospace and Electronic Systems Magazine 25 (7 PART 2): 5–28. doi:10.1109/MAES.2010.5546306.
   Web of Science ®Google Scholar
• Melit Devassy, B., and S. George. 2020. “Dimensionality Reduction and Visualisation of Hyperspectral Ink Data Using t-SNE.” Forensic Science International 311: 110194. doi:10.1016/j.forsciint.2020.110194.
   PubMed Web of Science ®Google Scholar
• Molero, J. M., E. M. Garzón, I. Garcia, and A. J. Plaza. 2012. “Anomaly Detection Based on a Parallel Kernel RX Algorithm for Multicore Platforms.” Journal of Applied Remote Sensing 6 (1): 061503.
   Google Scholar
• Nasrabadi, N. M. 2014. “Hyperspectral Target Detection: An Overview of Current and Future Challenges.” IEEE Signal Processing Magazine 31 (1): 34–44. doi:10.1109/MSP.2013.2278992.
   Web of Science ®Google Scholar
• Ng, A. 2011. “Sparse Autoencoder.” CS294A Lecture Notes 72: 1–19.
   Google Scholar
• Noble, W. S. 2006. “What Is a Support Vector Machine?” Nature Biotechnology 24 (12): 1565–1567.
   PubMed Web of Science ®Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222.
   Web of Science ®Google Scholar
• Pearson, K. 1901. “LIII. On Lines and Planes of Closest fit to Systems of Points in Space.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2 (11): 559–572.
   Google Scholar
• Qu, Y., W. Wang, R. Guo, B. Ayhan, C. Kwan, S. Vance, and H. Qi. 2018. “Hyperspectral Anomaly Detection through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition.” IEEE Transactions on Geoscience and Remote Sensing 56 (8): 4391–4405.
   Web of Science ®Google Scholar
• Racetin, I., and A. Krtalić. 2021. “Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications.” Applied Sciences 11 (11), doi:10.3390/app11114878.
   Google Scholar
• Rao, W., L. Gao, Y. Qu, X. Sun, B. Zhang, and J. Chanussot. 2022a. “Siamese Transformer Network for Hyperspectral Image Target Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–19. doi:10.1109/TGRS.2022.3163173.
   Web of Science ®Google Scholar
• Rao, W., Y. Qu, L. Gao, X. Sun, Y. Wu, and B. Zhang. 2022b. “Transferable Network with Siamese Architecture for Anomaly Detection in Hyperspectral Images.” International Journal of Applied Earth Observation and Geoinformation 106: 102669. doi:10.1016/j.jag.2021.102669.
   Web of Science ®Google Scholar
• Reed, I. S., and X. Yu. 1990. “Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution.” IEEE Transactions on Acoustics, Speech, and Signal Processing 38 (10): 1760–1770.
   Google Scholar
• Reynolds, D. A. 2009. “Gaussian Mixture Models.” Encyclopedia of Biometrics 741: 659–663.
   Google Scholar
• Rodarmel, C., and J. Shan. 2002. “Principal Component Analysis for Hyperspectral Image Classification.” Surveying and Land Information Science 62 (2): 115–122.
   Google Scholar
• Sakurada, M., and T. Yairi. 2014. “Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction.” ACM International Conference Proceeding Series, 02-December-2014, 4–11. doi:10.1145/2689746.2689747.
   Google Scholar
• Salem, M. Ben, K. S. Ettabaa, and M. A. Hamdi. 2014, November 5–7. “Anomaly Detection in Hyperspectral Imagery: An Overview.” IEEE 2014 First International Image Processing, Applications and Systems Conference (IPAS), Sfax, Tunisia, 1–6. doi:10.1109/ipas.2014.7043320
   Google Scholar
• Schaum, A. 2004. “Joint Subspace Detection of Hyperspectral Targets.” 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No. 04TH8720), 3.
   Google Scholar
• Schaum, A. P. 2007. “Hyperspectral Anomaly Detection Beyond RX.” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII 6565: 656502.
   Google Scholar
• Scholkopf, B., K.-K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik. 1997. “Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers.” IEEE Transactions on Signal Processing 45 (11): 2758–2765.
   Web of Science ®Google Scholar
• Schölkopf, B., R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt. 1999. “Support Vector Method for Novelty Detection.” NIPS 12: 582–588.
   Google Scholar
• Seeböck, P., S. Waldstein, S. Klimscha, B. S. Gerendas, R. Donner, T. Schlegl, U. Schmidt-Erfurth, and G. Langs. 2016. “Identifying and Categorizing Anomalies in Retinal Imaging Data.” ArXiv Preprint ArXiv:1612.00686.
   Google Scholar
• Shen, S. S., and E. M. Bassett III. 2002. “Information-Theory-Based Band Selection and Utility Evaluation for Reflective Spectral Systems.” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII 4725: 18–29.
   Google Scholar
• Smits, G. F., and E. M. Jordaan. 2002. “Improved SVM Regression Using Mixtures of Kernels.” Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290), 3, 2785–2790.
   Google Scholar
• Souza, C. R. 2010. “Kernel Functions for Machine Learning Applications.” Creative Commons Attribution-Noncommercial-Share Alike, 3, 29.
   Google Scholar
• Spearman, C. 1961. ““General Intelligence” Objectively Determined and Measured.” The American Journal of Psychology 15 (2): 201–293.
   Google Scholar
• Stein, D. W. J., S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker. 2002. “Anomaly Detection from Hyperspectral Imagery.” IEEE Signal Processing Magazine 19 (1): 58–69.
   Web of Science ®Google Scholar
• Su, H., Z. Wu, H. Zhang, and Q. Du. 2022. “Hyperspectral Anomaly Detection: A Survey.” IEEE Geoscience and Remote Sensing Magazine 10: 64–90. doi:10.1109/MGRS.2021.3105440.
   Web of Science ®Google Scholar
• Sun, W., C. Liu, J. Li, Y. M. Lai, and W. Li. 2014. “Low-Rank and Sparse Matrix Decomposition-Based Anomaly Detection for Hyperspectral Imagery.” Journal of Applied Remote Sensing 8 (1): 083641.
   Web of Science ®Google Scholar
• Sun, X., Y. Qu, L. Gao, X. Sun, H. Qi, B. Zhang, and T. Shen. 2021. “Target Detection through Tree-Structured Encoding for Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 59 (5): 4233–4249. doi:10.1109/TGRS.2020.3024852.
   Web of Science ®Google Scholar
• Sun, X., Y. Qu, L. Gao, X. Sun, H. Qi, B. Zhang, and T. Shen. 2022. “Ensemble-Based Information Retrieval with Mass Estimation for Hyperspectral Target Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–23. doi:10.1109/TGRS.2021.3075583.
   Web of Science ®Google Scholar
• Taghipour, A., and H. Ghassemian. 2019. “Unsupervised Hyperspectral Target Detection Using Spectral Residual of Deep Autoencoder Networks.” 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), 52–57.
   Google Scholar
• Tan, K., Z. Hou, F. Wu, Q. Du, and Y. Chen. 2019. “Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation.” Remote Sensing 11 (11): 1318.
   Web of Science ®Google Scholar
• Tang, Y. Y., Y. Lu, and H. Yuan. 2015. “Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform.” IEEE Transactions on Geoscience and Remote Sensing 53 (5): 2467–2480.
   Web of Science ®Google Scholar
• Tao, R., X. Zhao, W. Li, H.-C. Li, and Q. Du. 2019. “Hyperspectral Anomaly Detection by Fractional Fourier Entropy.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (12): 4920–4929.
   Web of Science ®Google Scholar
• Tenenbaum, J. B., V. De Silva, and J. C. Langford. 2000. “A Global Geometric Framework for Nonlinear Dimensionality Reduction.” Science 290 (5500): 2319–2323. doi:10.1126/science.290.5500.2319.
   PubMed Web of Science ®Google Scholar
• Theiler, J., and B. Wohlberg. 2012. “Local Coregistration Adjustment for Anomalous Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 50 (8): 3107–3116.
   Web of Science ®Google Scholar
• Thurstone, L. L. 1947, December 4. Multiple-Factor Analysis; A Development and Expansion of the Vectors of Mind. The Quarterly Review of Biology. Chicago: The University of Chicago Press.
   Google Scholar
• Tu, B., N. Li, Z. Liao, X. Ou, and G. Zhang. 2019. “Hyperspectral Anomaly Detection via Spatial Density Background Purification.” Remote Sensing 11(12): 2618. doi:10.3390/rs11222618.
   Google Scholar
• Tu, B., X. Yang, N. Li, C. Zhou, and D. He. 2020a. “Hyperspectral Anomaly Detection via Density Peak Clustering.” Pattern Recognition Letters 129: 144–149.
   Web of Science ®Google Scholar
• Tu, B., X. Yang, C. Zhou, D. He, and A. Plaza. 2020b. “Hyperspectral Anomaly Detection Using Dual Window Density.” IEEE Transactions on Geoscience and Remote Sensing 58 (12): 8503–8517.
   Web of Science ®Google Scholar
• Van Der Maaten, L., E. Postma, and J. den Herik. 2009. “Dimensionality Reduction: A Comparative.” Journal of Machine Learning Research 10 (66–71): 13.
   Google Scholar
• Verdoja, F., and M. Grangetto. 2020. “Graph Laplacian for Image Anomaly Detection.” Machine Vision and Applications 31 (1): 1–16.
   Web of Science ®Google Scholar
• Waheed, T., R. B. Bonnell, S. O. Prasher, and E. Paulet. 2006. “Measuring Performance in Precision Agriculture: CART—A Decision Tree Approach.” Agricultural Water Management 84 (1–2): 173–185.
   Web of Science ®Google Scholar
• Wall, M. E., A. Rechtsteiner, and L. M. Rocha. 2003. “Singular Value Decomposition and Principal Component Analysis.” In A Practical Approach to Microarray Data Analysis, edited by D. P. Berrar, W. Dubitzky, and M. Granzow, 91–109. Boston, MA: Springer.
   Google Scholar
• Wang, R., H. Hu, F. He, F. Nie, S. Cai, and Z. Ming. 2020. “Self-Weighted Collaborative Representation for Hyperspectral Anomaly Detection.” Signal Processing 177: 107718. doi:10.1016/j.sigpro.2020.107718.
   Web of Science ®Google Scholar
• Wang, Y.-X., J.-X. Liu, Y.-L. Gao, C.-H. Zheng, and J.-L. Shang. 2016. “Differentially Expressed Genes Selection via Laplacian Regularized Low-Rank Representation Method.” Computational Biology and Chemistry 65: 185–192.
   PubMed Web of Science ®Google Scholar
• Wang, Shuangting, and C. Wang. 2015. “Research on Dimension Reduction Method for Hyperspectral Remote Sensing Image Based on Global Mixture Coordination Factor Analysis.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 40: 159–167.
   Google Scholar
• Wang, Shaoyu, X. Wang, L. Zhang, and Y. Zhong. 2022a. “Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–14. doi:10.1109/TGRS.2021.3057721.
   Web of Science ®Google Scholar
• Wang, Shaoyu, X. Wang, L. Zhang, and Y. Zhong. 2022b. “Deep Low-Rank Prior for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–17. doi:10.1109/TGRS.2022.3165833.
   Google Scholar
• Wang, Y., H. Yao, and S. Zhao. 2016. “Auto-Encoder Based Dimensionality Reduction.” Neurocomputing 184: 232–242. doi:10.1016/j.neucom.2015.08.104.
   Web of Science ®Google Scholar
• Wold, S., K. Esbensen, and P. Geladi. 1987. “Principal Component Analysis.” Chemometrics and Intelligent Laboratory Systems 2 (1–3): 37–52.
   Web of Science ®Google Scholar
• Xie, W., T. Jiang, Y. Li, X. Jia, and J. Lei. 2019a. “Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 57 (7): 4218–4230.
   Web of Science ®Google Scholar
• Xie, W., J. Lei, B. Liu, Y. Li, and X. Jia. 2019b. “Spectral Constraint Adversarial Autoencoders Approach to Feature Representation in Hyperspectral Anomaly Detection.” Neural Networks 119: 222–234. doi:10.1016/j.neunet.2019.08.012.
   PubMed Web of Science ®Google Scholar
• Xie, W., B. Liu, Y. Li, J. Lei, C.-I. Chang, and G. He. 2020a. “Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 58 (4): 2352–2365.
   Web of Science ®Google Scholar
• Xie, W., B. Liu, Y. Li, J. Lei, and Q. Du. 2020b. “Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 58 (8): 5416–5427.
   Web of Science ®Google Scholar
• Xie, W., X. Zhang, Y. Li, J. Lei, J. Li, and Q. Du. 2021. “Weakly Supervised Low-Rank Representation for Hyperspectral Anomaly Detection.” IEEE Transactions on Cybernetics 51 (8): 3889–3900. doi:10.1109/TCYB.2021.3065070.
   PubMed Web of Science ®Google Scholar
• Xu, Y., Z. Wu, J. Li, A. Plaza, and Z. Wei. 2016. “Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 54 (4): 1990–2000.
   Web of Science ®Google Scholar
• Yang, C., J. K. Westbrook, C. P.-C. Suh, D. E. Martin, W. C. Hoffmann, Y. Lan, B. K. Fritz, and J. A. Goolsby. 2014. “An Airborne Multispectral Imaging System Based on Two Consumer-Grade Cameras for Agricultural Remote Sensing.” Remote Sensing 6 (6): 5257–5278.
   Web of Science ®Google Scholar
• Yousefan, M., H. E. Najafabadi, H. Amirkhani, H. Leung, and V. Hajihashemi. 2022. “Deep Anomaly Detection in Hyperspectral Images Based on Membership Maps and Object Area Filtering.” Expert Systems with Applications 191: 116200.
   Web of Science ®Google Scholar
• Yuan, Y., Q. Wang, and G. Zhu. 2015. “Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter.” IEEE Transactions on Geoscience and Remote Sensing 53 (2): 620–630.
   Web of Science ®Google Scholar
• Zhang, L., and B. Cheng. 2019. “A Stacked Autoencoders-Based Adaptive Subspace Model for Hyperspectral Anomaly Detection.” Infrared Physics & Technology 96: 52–60.
   Web of Science ®Google Scholar
• Zhang, W., X. Lu, and X. Li. 2019. “Similarity Constrained Convex Nonnegative Matrix Factorization for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 57 (7): 4810–4822.
   Web of Science ®Google Scholar
• Zhao, R., B. Du, and L. Zhang. 2014. “A Robust Nonlinear Hyperspectral Anomaly Detection Approach.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (4): 1227–1234.
   Web of Science ®Google Scholar
• Zhao, C., C. Li, S. Feng, N. Su, and W. Li. 2020. “A Spectral-Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 5982–5997. doi:10.1109/JSTARS.2020.3028372.
   Web of Science ®Google Scholar
• Zhao, C., Y. Wang, B. Qi, and J. Wang. 2015. “Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery.” Remote Sensing 7 (4): 3966–3985.
   Web of Science ®Google Scholar
• Zhao, C., and L. Zhang. 2018. “Spectral-Spatial Stacked Autoencoders Based on Low-Rank and Sparse Matrix Decomposition for Hyperspectral Anomaly Detection.” Infrared Physics & Technology 92: 166–176. doi:10.1016/j.infrared.2018.06.001.
   Web of Science ®Google Scholar
• Zhou, J., C. Kwan, B. Ayhan, and M. T. Eismann. 2016. “A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 54 (11): 6497–6504. doi:10.1109/TGRS.2016.2585495.
   Web of Science ®Google Scholar
• Zhou, T., and D. Tao. 2011. “Godec: Randomized Low-Rank & Sparse Matrix Decomposition in Noisy Case.” Proceedings of the 28th International Conference on Machine Learning, ICML 2011.
   Google Scholar
• Zhuang, L., L. Gao, B. Zhang, X. Fu, and J. M. Bioucas-Dias. 2021. “Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–17. doi:10.1109/TGRS.2020.3040221.
   Web of Science ®Google Scholar
• Zou, K. H., S. K. Warfield, A. Bharatha, C. M. C. Tempany, M. R. Kaus, S. J. Haker, W. M. Wells III, F. A. Jolesz, and R. Kikinis. 2004. “Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index1: Scientific Reports.” Academic Radiology 11 (2): 178–189.
   PubMed Web of Science ®Google Scholar