{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:11:43Z","timestamp":1787170303964,"version":"build-2736575974"},"reference-count":50,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The core challenge in hyperspectral image (HSI) classification lies in how to collaboratively model long\u2010range dependencies and local structural features. This paper introduces the Multiple Morphology Perception Mamba Model (MMP\u2010Mamba), which achieves a unified framework of global context awareness and local feature enhancement by deeply integrating dynamic morphological operations with a selective state space model. The model innovatively incorporates morphological priors by adaptively generating morphological kernels that enhance local details and suppress noise, while an attention mechanism dynamically fuses the original features with morphological gradients to focus on key regions. Additionally, a learnable gating network injects the morphologically enhanced features into the Mamba sequential modelling process, effectively compensating for local information loss caused by data serialisation. Experimental results on four benchmark datasets (Pavia University, Houston, HanChuan, and HongHu) demonstrate that MMP\u2010Mamba significantly outperforms existing mainstream methods. Specifically, in the Pavia University scenario, the overall accuracy, average accuracy, and Kappa coefficient are improved by 2.97%, 2.91%, and 3.12%, respectively, compared to the runner\u2010up model; in the HongHu crop sub\u2010classification task, the model markedly enhances the ability to differentiate morphologically similar crops. While maintaining linear computational complexity, this model provides a solution for HSI classification that combines high precision with high efficiency.<\/jats:p>","DOI":"10.1049\/ipr2.70291","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:53:02Z","timestamp":1769086382000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multiple Morphology Perception Mamba for Hyperspectral Image Classification"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0005-9241-3025","authenticated-orcid":false,"given":"Rui","family":"Cao","sequence":"first","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjian","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Geospatial Information Information Engineering University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haikun","family":"Yu","sequence":"additional","affiliation":[{"name":"HeNan Institute of Remote Sensing  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering China University of Geosciences  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109245"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19059-9_21"},{"issue":"1","key":"e_1_2_11_4_1","doi-asserted-by":"crossref","DOI":"10.59717\/j.xinn-geo.2024.100055","article-title":"Multimodal Artificial Intelligence Foundation Models: Unleashing the Power of Remote Sensing Big Data in Earth Observation","volume":"2","author":"Hong D.","year":"2024","journal-title":"Innovation Geoscience"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2024.100691"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3362475"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","unstructured":"M.Ahmad M.Usama S.Distefano andM.Mazzara \u201cHyperspectral Image Classification With Fuzzy Spatial\u2010spectral Class Discriminate Information \u201d inProceedings of the2024 IEEE International Conference on Image Processing (ICIP 2024) 2285\u20132291.","DOI":"10.1109\/ICIP51287.2024.10647404"},{"issue":"6","key":"e_1_2_11_8_1","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.11834\/jig.250045","article-title":"Overview and Prospects of Intelligent Classification of Hyperspectral Images","volume":"30","author":"Mingyi H.","year":"2025","journal-title":"Journal of Image and Graphics"},{"key":"e_1_2_11_9_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep Learning Classifiers for Hyperspectral Imaging: A Review [J\/OL]","author":"Paoletti M. 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