{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:41:02Z","timestamp":1779295262905,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["201901081002"],"award-info":[{"award-number":["201901081002"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51801142"],"award-info":[{"award-number":["51801142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Equipment Pre-research Key Laboratory Foundation","award":["6142107200208"],"award-info":[{"award-number":["6142107200208"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral anomaly detection has become an important branch of remote\u2013sensing image processing due to its important theoretical value and wide practical application prospects. However, some anomaly detection methods mainly exploit the spectral feature and do not make full use of spatial features, thus limiting the performance improvement of anomaly detection methods. Here, a novel hyperspectral anomaly detection method, called spectral\u2013spatial complementary decision fusion, is proposed, which combines the spectral and spatial features of a hyperspectral image (HSI). In the spectral dimension, the three\u2013dimensional Hessian matrix was first utilized to obtain three\u2013directional feature images, in which the background pixels of the HSI were suppressed. Then, to more accurately separate the sparse matrix containing the anomaly targets in the three\u2013directional feature images, low\u2013rank and sparse matrix decomposition (LRSMD) with truncated nuclear norm (TNN) was adopted to obtain the sparse matrix. After that, the rough detection map was obtained from the sparse matrix through finding the Mahalanobis distance. In the spatial dimension, two\u2013dimensional attribute filtering was employed to extract the spatial feature of HSI with a smooth background. The spatial weight image was subsequently obtained by fusing the spatial feature image. Finally, to combine the complementary advantages of each dimension, the final detection result was obtained by fusing all rough detection maps and the spatial weighting map. In the experiments, one synthetic dataset and three real\u2013world datasets were used. The visual detection results, the three\u2013dimensional receiver operating characteristic (3D ROC) curve, the corresponding two\u2013dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) were utilized as evaluation indicators. Compared with nine state\u2013of\u2013the\u2013art alternative methods, the experimental results demonstrate that the proposed method can achieve effective and excellent anomaly detection results.<\/jats:p>","DOI":"10.3390\/rs14040943","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T22:44:47Z","timestamp":1644965087000},"page":"943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Spectral\u2013Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-1894","authenticated-orcid":false,"given":"Pei","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1497-4810","authenticated-orcid":false,"given":"Jiangluqi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dabao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Spacecraft System Engineering, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, F., Zhang, X., Laakso, K., and Chan, J.C.-W. (2021). Archetypal analysis and structured sparse representation for hyperspectral anomaly detection. Remote Sens., 13.","DOI":"10.3390\/rs13204102"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tang, L., Li, Z., Wang, W., Zhao, B., Pan, Y., and Tian, Y. (2021). An efficient and robust framework for hyperspectral anomaly detection. Remote Sens., 13.","DOI":"10.3390\/rs13214247"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, L., Cen, Y., Chen, L., and Wang, Y. (2021). A fast hyperspectral anomaly detection algorithm based on greedy bilateral smoothing and extended multi-attribute profile. Remote Sens., 13.","DOI":"10.3390\/rs13193954"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cao, L., Wang, S., Gao, L., and Zhong, Y. (2021). Anomaly detection in airborne Fourier transform thermal infrared spectrometer images based on emissivity and a segmented low-rank prior. Remote Sens., 13.","DOI":"10.3390\/rs13040754"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"026514","DOI":"10.1117\/1.JRS.14.026514","article-title":"Feature extraction approach for quality assessment of remotely sensed hyperspectral images","volume":"14","author":"Das","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1109\/LGRS.2020.2993214","article-title":"Anomaly detection of hyperspectral image via tensor completion","volume":"18","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Z., He, F., Hu, H., Wang, F., and Yu, W. (2021). Random collective representation-based detector with multiple features for hyperspectral images. Remote Sens., 13.","DOI":"10.3390\/rs13040721"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xiang, P., Song, J., Li, H., Gu, L., and Zhou, H. (2019). Hyperspectral anomaly detection with harmonic analysis and low-rank decomposition. Remote Sens., 11.","DOI":"10.3390\/rs11243028"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.asr.2013.04.002","article-title":"Mapping regolith and gossan for mineral exploration in the eastern Kumaon Himalaya, India using hyperion data and object oriented image classification","volume":"53","author":"Farooq","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/JSTARS.2016.2635482","article-title":"Mapping mosaic virus in sugarcane based on hyperspectral images","volume":"10","author":"Moriya","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2004.07.004","article-title":"Evaluation of hyperspectral remote sensing as a means of environmental monitoring in the St. Austell China clay (kaolin) region, Cornwall, UK","volume":"93","author":"Ellis","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.patcog.2017.11.024","article-title":"Material based salient object detection from hyperspectral images","volume":"76","author":"Liang","year":"2018","journal-title":"Pattern Recogn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.infrared.2018.06.001","article-title":"Spectral\u2013spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection","volume":"92","author":"Zhao","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1080\/01431161.2017.1312620","article-title":"A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection","volume":"38","author":"Zhang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/JSTARS.2014.2315772","article-title":"An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery","volume":"7","author":"Matteoli","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. 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 Observ. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TGRS.2004.841481","article-title":"A cluster-based approach for detecting man-made objects and changes in imagery","volume":"43","author":"Carlotto","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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 Observ. Remote Sens."},{"key":"ref_20","unstructured":"Liu, J., Hou, Z., Li, W., Tao, R., Orlando, D., and Li, H. (2021). Multipixel anomaly detection with unknown patterns for hyperspectral imagery. IEEE Trans. Neural Netw. Learn. Syst., 1\u201311."},{"key":"ref_21","first-page":"5516222","article-title":"Component decomposition analysis for hyperspectral anomaly detection","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5513617","DOI":"10.1109\/TGRS.2021.3116681","article-title":"Ensemble entropy metric for hyperspectral anomaly detection","volume":"60","author":"Tu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1109\/TGRS.2016.2585495","article-title":"A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images","volume":"54","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"107585","DOI":"10.1016\/j.sigpro.2020.107585","article-title":"Joint sparse-collaborative representation to fuse hyperspectral and multispectral images","volume":"173","author":"Xing","year":"2020","journal-title":"Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/TGRS.2020.3001353","article-title":"Autonomous endmember detection via an abundance anomaly guided saliency prior for hyperspectral imagery","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/JSTSP.2011.2113170","article-title":"Sparse representation for target detection in hyperspectral imagery","volume":"5","author":"Chen","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","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":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/LGRS.2020.2998809","article-title":"Hyperspectral anomaly detection via integration of feature extraction and background purification","volume":"18","author":"Ma","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4391","DOI":"10.1109\/TGRS.2018.2818159","article-title":"Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition","volume":"56","author":"Qu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, L., Li, W., Qu, Y., Zhao, C., Tao, R., and Du, Q. (2022). Prior-based tensor approximation for anomaly detection in hyperspectral imagery. IEEE Trans. Neural Netw. Learn. Syst., 1\u201314.","DOI":"10.1109\/TNNLS.2020.3038659"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"78658","DOI":"10.1109\/ACCESS.2021.3083060","article-title":"Machine learning for anomaly detection: A systematic review","volume":"9","author":"Nassif","year":"2021","journal-title":"IEEE Access."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Racetin, I., and Krtali, A. (2021). Systematic review of anomaly detection in hyperspectral remote sensing applications. Appl. Sci., 11.","DOI":"10.3390\/app11114878"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1080\/01431161.2019.1708504","article-title":"Hyperspectral anomaly detection by local joint subspace process and support vector machine","volume":"41","author":"Xiang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1109\/TGRS.2019.2956159","article-title":"Hyperspectral band selection for spectral-spatial anomaly detection","volume":"58","author":"Xie","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.sigpro.2015.09.037","article-title":"A spectral-spatial based local summation anomaly detection method for hyperspectral images","volume":"124","author":"Du","year":"2016","journal-title":"Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.1109\/TGRS.2016.2572400","article-title":"A tensor decomposition-based anomaly detection algorithm for hyperspectral image","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8131","DOI":"10.1109\/TGRS.2019.2918387","article-title":"Spectral-spatial feature extraction for hyperspectral anomaly detection","volume":"57","author":"Lei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.infrared.2018.05.028","article-title":"Hyperspectral anomaly detection based on the bilateral filter","volume":"92","author":"Yao","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5982","DOI":"10.1109\/JSTARS.2020.3028372","article-title":"A spectral-spatial 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. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1008045108935","article-title":"Feature detection with automatic scale selection","volume":"30","author":"Lindeberg","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/LGRS.2018.2800034","article-title":"A saliency-based band selection approach for hyperspectral imagery inspired by scale selection","volume":"15","author":"Su","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1002\/jmri.1880070234","article-title":"Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correlation","volume":"7","author":"Du","year":"1997","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1080\/2150704X.2012.749361","article-title":"Automated tree crown detection and size estimation using multi-scale analysis of high-resolution satellite imagery","volume":"4","author":"Skurikhin","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TPAMI.2012.271","article-title":"Fast and accurate matrix completion via truncated nuclear norm regularization","volume":"35","author":"Hu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neunet.2016.09.005","article-title":"Recovering low-rank and sparse matrix based on the truncated nuclear norm","volume":"85","author":"Cao","year":"2017","journal-title":"Neural Netw."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00371-017-1456-8","article-title":"Color image denoising via monogenic matrix-based sparse representation","volume":"35","author":"Gai","year":"2019","journal-title":"Vis. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/JPROC.2010.2044470","article-title":"Sparse representation for computer vision and pattern recognition","volume":"98","author":"Wright","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_57","unstructured":"Zhang, D., Hu, Y., Ye, J., Li, X., and He, X. (2012, January 16\u201321). Matrix completion by truncated nuclear norm regularization. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1007\/s00371-018-1555-1","article-title":"Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer","volume":"35","author":"Xue","year":"2019","journal-title":"Vis. Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kong, W., Song, Y., and Liu, J. (2021). Hyperspectral image denoising via framelet transformation based three-modal tensor nuclear norm. Remote Sens., 13.","DOI":"10.3390\/rs13193829"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/76.709404","article-title":"Approximate convolution using DCT coefficient multipliers","volume":"8","author":"Merhav","year":"1998","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Andika, F., Rizkinia, M., and Okuda, M. (2020). A hyperspectral anomaly detection algorithm based on morphological profile and attribute filter with band selection and automatic determination of maximum area. Remote Sens., 12.","DOI":"10.3390\/rs12203387"},{"key":"ref_62","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_63","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_64","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wen, G., and Qiu, S. (2018). Low-rank and sparse matrix decomposition with cluster weighting for hyperspectral anomaly detection. Remote Sens., 10.","DOI":"10.3390\/rs10050707"},{"key":"ref_65","unstructured":"Du, X., and Zare, A. (2017). Technical Report: Scene Label Ground Truth Map for MUUFL Gulfport Data Set, University of Florida Technical Report. Tech. Rep. 20170417."},{"key":"ref_66","first-page":"1","article-title":"Airborne hyperspectral data over Chikusei","volume":"SAL-2016-05-27","author":"Yokoya","year":"2016","journal-title":"Space Appl. Lab. Univ. Tokyo Jpn."},{"key":"ref_67","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/943\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:20:11Z","timestamp":1760134811000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/943"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,15]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14040943"],"URL":"https:\/\/doi.org\/10.3390\/rs14040943","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,15]]}}}