{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:03:58Z","timestamp":1769879038796,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901082"],"award-info":[{"award-number":["61901082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LH2019F001"],"award-info":[{"award-number":["LH2019F001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Heilongjiang Province in China","award":["61901082"],"award-info":[{"award-number":["61901082"]}]},{"name":"Natural Science Foundation of Heilongjiang Province in China","award":["LH2019F001"],"award-info":[{"award-number":["LH2019F001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, using spatial\u2013spectral information for hyperspectral anomaly detection (AD) has received extensive attention. However, the test point and its neighborhood points are usually treated equally without highlighting the test point, which is unreasonable. In this paper, improved central attention network-based tensor RX (ICAN-TRX) is designed to extract hyperspectral anomaly targets. The ICAN-TRX algorithm consists of two parts, ICAN and TRX. In ICAN, a test tensor block as a value tensor is first reconstructed by DBN to make the anomaly points more prominent. Then, in the reconstructed tensor block, the central tensor is used as a convolution kernel to perform convolution operation with its tensor block. The result tensor as a key tensor is transformed into a weight matrix. Finally, after the correlation operation between the value tensor and the weight matrix, the new test point is obtained. In ICAN, the spectral information of a test point is emphasized, and the spatial relationships between the test point and its neighborhood points reflect their similarities. TRX is used in the new HSI after ICAN, which allows more abundant spatial information to be used for AD. Five real hyperspectral datasets are selected to estimate the performance of the proposed ICAN-TRX algorithm. The detection results demonstrate that ICAN-TRX achieves superior performance compared with seven other AD algorithms.<\/jats:p>","DOI":"10.3390\/rs14225865","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"5865","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improved Central Attention Network-Based Tensor RX for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Lili","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiachen","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baohong","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudan","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengpin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent Advances in Techniques for Hyperspectral Image Processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral Image Data Analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A Survey on Object Detection in Optical Remote Sensing Images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1109\/TGRS.2014.2326654","article-title":"Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter","volume":"53","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TGRS.2002.800280","article-title":"Anomaly Detection and Classification for Hyperspectral Imagery","volume":"40","author":"Chang","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MAES.2010.5546306","article-title":"A Tutorial Overview of Anomaly Detection in Hyperspectral Images","volume":"25","author":"Matteoli","year":"2010","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_8","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."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/JSTARS.2013.2238609","article-title":"Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data","volume":"6","author":"Molero","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1109\/LGRS.2014.2306209","article-title":"Local Sparsity Divergence for Hyperspectral Anomaly Detection","volume":"11","author":"Yuan","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/LGRS.2016.2552403","article-title":"Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising","volume":"13","author":"Meng","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","first-page":"753","article-title":"Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding","volume":"31","author":"Ma","year":"2010","journal-title":"J Infrared Millim. Terahertz Waves"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, L., Crawford, M.M., and Tian, J. (2010, January 25\u201330). Anomaly Detection for Hyperspectral Images Using Local Tangent Space Alignment. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652183"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"025026","DOI":"10.1117\/1.JRS.10.025026","article-title":"Sparsity Divergence Index Based on Locally Linear Embedding for Hyperspectral Anomaly Detection","volume":"10","author":"Zhang","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/JSTARS.2017.2655516","article-title":"R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method","volume":"10","author":"Pan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/JSTARS.2016.2598859","article-title":"Active Deep Learning for Classification of Hyperspectral Images","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LGRS.2017.2657818","article-title":"Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/LGRS.2019.2962582","article-title":"Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised spectral\u2013spatialfeature learning with stacked sparse autoencoder for hyperspectral imagery classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.1109\/JSTARS.2016.2517204","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder","volume":"9","author":"Ma","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Data Based on Deep Belief Network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.infrared.2018.11.015","article-title":"A Stacked Autoencoders-Based Adaptive Subspace Model for Hyperspectral Anomaly Detection","volume":"96","author":"Zhang","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104314","DOI":"10.1016\/j.infrared.2022.104314","article-title":"Hyperspectral Anomaly Detection via Fractional Fourier Transform and Deep Belief Networks","volume":"125","author":"Zhang","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Plaza, A., Mart\u00edn, G., Plaza, J., Zortea, M., and S\u00e1nchez, S. (2011). Recent Developments in Endmember Extraction and Spectral Unmixing. Optical Remote Sensing, Springer.","DOI":"10.1007\/978-3-642-14212-3_12"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/1687-6180-2013-186","article-title":"Survey of Hyperspectral Image Denoising Methods Based on Tensor Decompositions","volume":"2013","author":"Lin","year":"2013","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6869","DOI":"10.1038\/srep06869","article-title":"A High-Order Statistical Tensor Based Algorithm for Anomaly Detection in Hyperspectral Imagery","volume":"4","author":"Geng","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, W., Qi, H., Ayhan, B., Kwan, C., and Vance, S. (2015, January 27\u201330). Low-Rank Tensor Decomposition Based Anomaly Detection for Hyperspectral Imagery. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351663"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1109\/TGRS.2009.2020910","article-title":"Target Detection with Semisupervised Kernel Orthogonal Subspace Projection","volume":"47","author":"Capobianco","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1080\/01431161.2017.1421795","article-title":"A Tensor-Based Adaptive Subspace Detector for Hyperspectral Anomaly Detection","volume":"39","author":"Zhang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, L., Ma, J., Cheng, B., and Lin, F. (2022). Fractional Fourier Transform Based Tensor RX for Hyperspectral Anomaly Detection. Remote Sens., 14.","DOI":"10.3390\/rs14030797"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation from Transformers","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"115663","DOI":"10.1016\/j.eswa.2021.115663","article-title":"An Attention-Driven Convolutional Neural Network-Based Multi-Level Spectral\u2013Spatial Feature Learning for Hyperspectral Image Classification","volume":"185","author":"Pu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_45","first-page":"1","article-title":"Central Attention Network for Hyperspectral Imagery Classification","volume":"185","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5557","DOI":"10.1109\/TNNLS.2021.3071026","article-title":"Multipixel Anomaly Detection with Unknown Patterns for Hyperspectral Imagery","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Horn, R.A., and Johnson, C.R. (1985). Matrix Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511810817"},{"key":"ref_48","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5865\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:46Z","timestamp":1760145706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,19]]},"references-count":48,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225865"],"URL":"https:\/\/doi.org\/10.3390\/rs14225865","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,19]]}}}