{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T14:07:15Z","timestamp":1780668435641,"version":"3.54.1"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.dsp.2026.106228","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:41:21Z","timestamp":1778758881000},"page":"106228","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Mamba-based multi-view efficient linear unmixing network for hyperspectral images"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9144-3199","authenticated-orcid":false,"given":"Lina","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1093-6502","authenticated-orcid":false,"given":"Yuquan","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.dsp.2026.106228_bib0001","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":"10.1016\/j.dsp.2026.106228_bib0002","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1016\/j.rse.2009.03.001","article-title":"Earth system science related imaging spectroscopy\u2014an assessment","volume":"113","author":"Schaepman","year":"2009","journal-title":"Remote Sens. Environ."},{"issue":"3","key":"10.1016\/j.dsp.2026.106228_bib0003","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/JPROC.2012.2196249","article-title":"Using high-resolution airborne and satellite imagery to assess crop growth and yield variability for precision agriculture","volume":"101","author":"Yang","year":"2013","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.dsp.2026.106228_bib0004","series-title":"2013 6th International Conference on Recent Advances in Space Technologies (RAST)","first-page":"171","article-title":"A short survey of hyperspectral remote sensing applications in agriculture","author":"Teke","year":"2013"},{"issue":"4","key":"10.1016\/j.dsp.2026.106228_bib0005","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."},{"issue":"2","key":"10.1016\/j.dsp.2026.106228_bib0006","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/LGRS.2019.2919755","article-title":"Fourier-based rotation-invariant feature boosting: an efficient framework for geospatial object detection","volume":"17","author":"Wu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"10.1016\/j.dsp.2026.106228_bib0007","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.dsp.2026.106228_bib0008","series-title":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","first-page":"1","article-title":"Modal mineralogy of planetary surfaces from visible and near-infrared spectral data","author":"Poulet","year":"2010"},{"issue":"1","key":"10.1016\/j.dsp.2026.106228_bib0009","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"10.1016\/j.dsp.2026.106228_bib0010","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Bioucas-Dias","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"4","key":"10.1016\/j.dsp.2026.106228_bib0011","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: a fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"10.1016\/j.dsp.2026.106228_bib0012","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.dsp.2026.106228_bib0013","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/TIP.2018.2878958","article-title":"An augmented linear mixing model to address spectral variability for hyperspectral unmixing","volume":"28","author":"Hong","year":"2019","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.dsp.2026.106228_bib0014","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","article-title":"Nonnegative matrix factorization: a comprehensive review","volume":"25","author":"Wang","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.dsp.2026.106228_bib0015","unstructured":"W. Yin, K. Kann, M. Yu, H. Sch\u00fctze, Comparative study of CNN and RNN for natural language processing, 2017. arXiv: 1702.01923."},{"key":"10.1016\/j.dsp.2026.106228_bib0016","first-page":"1","article-title":"Spectral\u2013spatial fusion sub-pixel mapping based on deep neural network","volume":"19","author":"He","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.dsp.2026.106228_bib0017","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/JSTARS.2023.3335907","article-title":"Hyperspectral unmixing with multi-scale convolution attention network","volume":"17","author":"Hu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0018","first-page":"1","article-title":"MAT-Net: multiscale aggregation transformer network for hyperspectral unmixing","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0019","first-page":"1","article-title":"Hyperspectral unmixing using transformer network","volume":"60","author":"Ghosh","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0020","first-page":"1","article-title":"A spectral-spatial attention network for hyperspectral unmixing","volume":"63","author":"Tao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0021","first-page":"1","article-title":"A new dual-feature fusion network for enhanced hyperspectral unmixing","volume":"62","author":"Tao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0022","series-title":"Advances in Neural Information Processing Systems","first-page":"572","article-title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","volume":"34","author":"Gu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106228_bib0023","unstructured":"A. Gu, K. Goel, C. R\u00e9, Efficiently modeling long sequences with structured state spaces, 2022. arXiv: 2111.00396."},{"issue":"6","key":"10.1016\/j.dsp.2026.106228_bib0024","doi-asserted-by":"crossref","DOI":"10.1145\/3767728","article-title":"A comprehensive survey on multi-view classification: methods, applications, and challenges","volume":"16","author":"Berahmand","year":"2025","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.dsp.2026.106228_bib0025","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TGRS.2018.2856929","article-title":"EndNet: sparse AutoEncoder network for endmember extraction and hyperspectral unmixing","volume":"57","author":"Ozkan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.dsp.2026.106228_bib0026","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TGRS.2020.2992743","article-title":"Convolutional autoencoder for spectral\u2013spatial hyperspectral unmixing","volume":"59","author":"Palsson","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0027","first-page":"1","article-title":"CyCU-Net: cycle-consistency unmixing network by learning cascaded autoencoders","volume":"60","author":"Gao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0028","first-page":"1","article-title":"SSCU-Net: spatial\u2013spectral collaborative unmixing network for hyperspectral images","volume":"60","author":"Qi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0029","first-page":"1","article-title":"Window transformer convolutional autoencoder for hyperspectral sparse unmixing","volume":"20","author":"Kong","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.dsp.2026.106228_bib0030","unstructured":"L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, X. Wang, Vision Mamba: efficient visual representation learning with bidirectional state space model, 2024. arXiv: 2401.09417."},{"key":"10.1016\/j.dsp.2026.106228_bib0031","series-title":"Advances in Neural Information Processing Systems","first-page":"103031","article-title":"VMamba: visual state space model","volume":"37","author":"Liu","year":"2024"},{"key":"10.1016\/j.dsp.2026.106228_bib0032","unstructured":"J. Ma, F. Li, B. Wang, U-Mamba: enhancing long-range dependency for biomedical image segmentation, 2024. arXiv: 2401.04722."},{"key":"10.1016\/j.dsp.2026.106228_bib0033","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"578","article-title":"SegMamba: long-range sequential modeling Mamba for 3D medical image segmentation","author":"Xing","year":"2024"},{"key":"10.1016\/j.dsp.2026.106228_bib0034","first-page":"1","article-title":"RSMamba: remote sensing image classification with state space model","volume":"21","author":"Chen","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.dsp.2026.106228_bib0035","unstructured":"J. Yao, D. Hong, C. Li, J. Chanussot, SpectralMamba: efficient Mamba for hyperspectral image classification, 2024. arXiv: 2404.08489."},{"key":"10.1016\/j.dsp.2026.106228_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109040","article-title":"HyOCNN: hybrid-optimized convolutional neural network for robust image classification","volume":"113","author":"Abdalla","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.dsp.2026.106228_bib0037","unstructured":"A. Gu, T. Dao, Mamba: linear-time sequence modeling with selective state spaces, 2024. arXiv: 2312.00752."},{"key":"10.1016\/j.dsp.2026.106228_bib0038","unstructured":"A. Bercovich, M. Dabbah, O. Puny, I. Galil, A. Geifman, Y. Geifman, I. Golan, E. Karpas, I. Levy, Z. Moshe, N. Nabwani, T. Ronen, I. Schen, E. Segal, I. Shahaf, O. Tropp, R. Zilberstein, R. El-Yaniv, FFN fusion: rethinking sequential computation in large language models, 2025. arXiv: 2503.18908."},{"key":"10.1016\/j.dsp.2026.106228_bib0039","unstructured":"K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, L. Kaiser, D. Belanger, L. Colwell, A. Weller, Rethinking attention with performers, 2022. arXiv: 2009.14794."},{"key":"10.1016\/j.dsp.2026.106228_bib0040","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"5156","article-title":"Transformers are RNNs: fast autoregressive transformers with linear attention","volume":"119","author":"Katharopoulos","year":"2020"},{"key":"10.1016\/j.dsp.2026.106228_bib0041","series-title":"Coastal Ocean Remote Sensing","first-page":"66800P","article-title":"Spatial and spectral resolution considerations for imaging coastal waters","volume":"6680","author":"Davis","year":"2007"},{"key":"10.1016\/j.dsp.2026.106228_bib0042","doi-asserted-by":"crossref","first-page":"5741","DOI":"10.1109\/JSTARS.2026.3655512","article-title":"SSST-GAN: a sampling-based spatial-spectral transformer and generative adversarial network for hyperspectral unmixing","volume":"19","author":"Zhang","year":"2026","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.dsp.2026.106228_bib0043","doi-asserted-by":"crossref","DOI":"10.1186\/s12864-026-12568-3","article-title":"DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network","author":"Yuan","year":"2026","journal-title":"BMC Genom."},{"issue":"2","key":"10.1016\/j.dsp.2026.106228_bib0044","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1013954","article-title":"SpaLSTF: diffusion-based generative model with BiLSTM and XCA-transformer for spatial transcriptomics imputation","volume":"22","author":"Yuan","year":"2026","journal-title":"PLOS Comput. Biol."}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426003477?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426003477?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:08:03Z","timestamp":1780664883000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426003477"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":44,"alternative-id":["S1051200426003477"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106228","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mamba-based multi-view efficient linear unmixing network for hyperspectral images","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106228","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106228"}}