{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:32:30Z","timestamp":1776393150032,"version":"3.51.2"},"reference-count":75,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YWF-21-JC-0"],"award-info":[{"award-number":["YWF-21-JC-0"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate. However, the traditional CR presents a low utilization rate for deep latent features in hyperspectral images, making the dictionary construction and the optimization of weight matrix sub-optimal. Due to the excellent capacity of neural networks for generation, we formulate the deep learning-based method into CR optimization in both global and local streams, and propose a novel hyperspectral anomaly detection method based on collaborative representation neural networks (CRNN) in this paper. In order to gain a complete background dictionary and avoid the pollution of anomalies, the global dictionary is collected in the global stream by optimizing the dictionary atom loss, while the local background dictionary is obtained by using a sliding dual window. Based on the two dictionaries, our two-stream networks are trained to learn the global and local representation of hyperspectral data by optimizing the objective function of CR. The detection result is calculated by the fusion of residual maps of original and represented data in the two streams. In addition, an autoencoder is introduced to obtain the hidden feature considered as the dense expression of the original hyperspectral image, and a feature extraction network is concerned to further learn the comprehensive features. Compared with the shallow learning CR, the proposed CRNN learns the dictionary and the representation weight matrix in neural networks to increase the detection performance, and the fixed network parameters instead of the complex matrix operations in traditional CR bring a high inference efficiency. The experiments on six public hyperspectral datasets prove that our proposed CRNN presents the state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/rs15133357","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuxiao","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Aerospace Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Tongbin","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Wuhan Digital Engineering Institute, Wuhan 430205, China"}]},{"given":"Jinshen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/JPROC.2009.2013561","article-title":"Automated Hyperspectral Cueing for Civilian Search and Rescue","volume":"97","author":"Eismann","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.isprsjprs.2015.01.006","article-title":"Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions","volume":"105","author":"Tuia","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3778","DOI":"10.1109\/TGRS.2019.2957135","article-title":"Ensemble learning for hyperspectral image classification using tangent collaborative representation","volume":"58","author":"Su","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2013.2278992","article-title":"Hyperspectral target detection: An overview of current and future challenges","volume":"31","author":"Nasrabadi","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2016.05.008","article-title":"Target detection in hyperspectral imagery using forward modeling and in-scene information","volume":"119","author":"Axelsson","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.isprsjprs.2018.08.012","article-title":"Multiple instance hybrid estimator for hyperspectral target characterization and sub-pixel target detection","volume":"146","author":"Jiao","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"Hyperspectral Anomaly Detection: A Dual Theory of Hyperspectral Target Detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"1","article-title":"Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","first-page":"1","article-title":"Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3889","DOI":"10.1109\/TCYB.2021.3065070","article-title":"Weakly Supervised Low-Rank Representation for Hyperspectral Anomaly Detection","volume":"51","author":"Xie","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2021.3105440","article-title":"Hyperspectral anomaly detection: A survey","volume":"10","author":"Su","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/TGRS.2004.841487","article-title":"Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery","volume":"43","author":"Kwon","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Borghys, D., K\u00e5sen, I., Achard, V., and Perneel, C. (2012, January 24). Comparative evaluation of hyperspectral anomaly detectors in different types of background. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, Baltimore, MD, USA.","DOI":"10.1117\/12.920387"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"097297","DOI":"10.1117\/1.JRS.9.097297","article-title":"Decision fusion for dual-window-based hyperspectral anomaly detector","volume":"9","author":"Li","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2014.2302446","article-title":"Weighted-RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery","volume":"7","author":"Guo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","first-page":"1","article-title":"Effective Anomaly Space for Hyperspectral Anomaly Detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TGRS.2004.841481","article-title":"A cluster-based approach for detecting human-made objects and changes in imagery","volume":"43","author":"Carlotto","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"033546","DOI":"10.1117\/1.3236689","article-title":"Anomaly detection in hyperspectral imagery: Comparison of methods using diurnal and seasonal data","volume":"3","author":"Hytla","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/TGRS.2006.873019","article-title":"A support vector method for anomaly detection in hyperspectral imagery","volume":"44","author":"Banerjee","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Messinger, D.W., and Albano, J. (2011, January 6\u20139). A graph theoretic approach to anomaly detection in hyperspectral imagery. Proceedings of the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080899"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TCYB.2015.2497711","article-title":"Hyperspectral anomaly detection by graph pixel selection","volume":"46","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4920","DOI":"10.1109\/JSTARS.2019.2940278","article-title":"Hyperspectral Anomaly Detection by Fractional Fourier Entropy","volume":"12","author":"Tao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1109\/TGRS.2017.2710145","article-title":"Hyperspectral anomaly detection with attribute and edge-preserving filters","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1109\/TGRS.2018.2890212","article-title":"Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection","volume":"57","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wen, M., Gao, C., and Wang, Y. (2022). Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering. Remote Sens., 14.","DOI":"10.3390\/rs14122730"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shang, W., Jouni, M., Wu, Z., Xu, Y., Dalla Mura, M., and Wei, Z. (2023). Hyperspectral Anomaly Detection Based on Regularized Background Abundance Tensor Decomposition. Remote Sens., 15.","DOI":"10.3390\/rs15061679"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/TNNLS.2020.3038659","article-title":"Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_30","first-page":"1","article-title":"Iterative Spectral\u2013Spatial Hyperspectral Anomaly Detection","volume":"61","author":"Chang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1109\/JSTARS.2015.2437073","article-title":"Hyperspectral anomaly detection by the use of background joint sparse representation","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4050","DOI":"10.1109\/TGRS.2018.2821168","article-title":"Exploiting structured sparsity for hyperspectral anomaly detection","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Soofbaf, S.R., Sahebi, M.R., and Mojaradi, B. (2018). A sliding window-based joint sparse representation (swjsr) method for hyperspectral anomaly detection. Remote Sens., 10.","DOI":"10.3390\/rs10030434"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TGRS.2014.2343955","article-title":"Collaborative representation for hyperspectral anomaly detection","volume":"53","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vafadar, M., and Ghassemian, H. (2017, January 19\u201320). Hyperspectral anomaly detection using outlier removal from collaborative representation. Proceedings of the 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), Shahrekord, Iran.","DOI":"10.1109\/PRIA.2017.7983039"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5029","DOI":"10.1109\/JSTARS.2018.2880749","article-title":"Hyperspectral anomaly detection using collaborative representation with outlier removal","volume":"11","author":"Su","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tu, B., Li, N., Liao, Z., Ou, X., and Zhang, G. (2019). Hyperspectral anomaly detection via spatial density background purification. Remote Sens., 11.","DOI":"10.3390\/rs11222618"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tan, K., Hou, Z., Wu, F., Du, Q., and Chen, Y. (2019). Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation. Remote Sens., 11.","DOI":"10.3390\/rs11111318"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5982","DOI":"10.1109\/JSTARS.2020.3028372","article-title":"A Spectral\u2013Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3228927","article-title":"Hyperspectral Anomaly Detection with Relaxed Collaborative Representation","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"083641","DOI":"10.1117\/1.JRS.8.083641","article-title":"Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery","volume":"8","author":"Sun","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1109\/TGRS.2015.2493201","article-title":"Anomaly detection in hyperspectral images based on low-rank and sparse representation","volume":"54","author":"Xu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1109\/TGRS.2015.2479299","article-title":"A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection","volume":"54","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2019.2936609","article-title":"Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection","volume":"58","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TCYB.2020.2968750","article-title":"Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection","volume":"51","author":"Li","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1002\/(SICI)1099-1239(199611)6:9\/10<983::AID-RNC263>3.0.CO;2-C","article-title":"Induced L2-norm control for LPV systems with bounded parameter variation rates","volume":"6","author":"Wu","year":"1996","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"ref_48","unstructured":"Zhou, T., and Tao, D. (July, January 28). Godec: Randomized low-rank & sparse matrix decomposition in noisy case. Proceedings of the Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jiang, K., Xie, W., Lei, J., Jiang, T., and Li, Y. (2021, January 2\u20139). LREN: Low-rank embedded network for sample-free hyperspectral anomaly detection. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v35i5.16536"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hu, X., Xie, C., Fan, Z., Duan, Q., Zhang, D., Jiang, L., Wei, X., Hong, D., Li, G., and Zeng, X. (2022). Hyperspectral anomaly detection using deep learning: A review. Remote Sens., 14.","DOI":"10.3390\/rs14091973"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8131","DOI":"10.1109\/TGRS.2019.2918387","article-title":"Spectral\u2013spatial feature extraction for hyperspectral anomaly detection","volume":"57","author":"Lei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/TGRS.2019.2944419","article-title":"Exploiting Embedding Manifold of Autoencoders for Hyperspectral Anomaly Detection","volume":"58","author":"Lu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Arisoy, S., Nasrabadi, N.M., and Kayabol, K. (2021, January 18\u201321). GAN-based hyperspectral anomaly detection. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287675"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4666","DOI":"10.1109\/TGRS.2020.2965961","article-title":"Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection","volume":"58","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/TGRS.2019.2948177","article-title":"Spectral adversarial feature learning for anomaly detection in hyperspectral imagery","volume":"58","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","first-page":"1","article-title":"Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","first-page":"1","article-title":"Spectral Distribution-Aware Estimation Network for Hyperspectral Anomaly Detection","volume":"60","author":"Xie","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_59","first-page":"3","article-title":"Autoencoders, minimum description length and Helmholtz free energy","volume":"6","author":"Hinton","year":"1994","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_60","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","first-page":"1","article-title":"Hyperspectral Anomaly Detection with Guided Autoencoder","volume":"60","author":"Xiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5416","DOI":"10.1109\/TGRS.2020.2965995","article-title":"Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection","volume":"58","author":"Xie","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","first-page":"1","article-title":"Sparse Coding-Inspired GAN for Hyperspectral Anomaly Detection in Weakly Supervised Learning","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","unstructured":"Huber, P.J. (1992). Breakthroughs in Statistics, Springer."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018, January 8\u201314). Group normalization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_67","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the ICML, Atlanta, GA, USA."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_69","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 16\u201318). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_70","unstructured":"Jang, E., Gu, S., and Poole, B. (2016). Categorical reparameterization with gumbel-softmax. arXiv."},{"key":"ref_71","first-page":"1299","article-title":"Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village)","volume":"24","author":"Senling","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2013.11.014","article-title":"Structured sparse method for hyperspectral unmixing","volume":"88","author":"Zhu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1109\/TGRS.2020.3021671","article-title":"An Effective Evaluation Tool for Hyperspectral Target Detection: 3D Receiver Operating Characteristic Curve Analysis","volume":"59","author":"Chang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","first-page":"1","article-title":"Comprehensive Analysis of Receiver Operating Characteristic (ROC) Curves for Hyperspectral Anomaly Detection","volume":"60","author":"Chang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:04:20Z","timestamp":1760126660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":75,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133357"],"URL":"https:\/\/doi.org\/10.3390\/rs15133357","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}