{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:27:26Z","timestamp":1774121246705,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR202103050458"],"award-info":[{"award-number":["ZR202103050458"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["1020714"],"award-info":[{"award-number":["1020714"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Start up fundation for doctoral research of Zaozhuang University","award":["ZR202103050458"],"award-info":[{"award-number":["ZR202103050458"]}]},{"name":"Start up fundation for doctoral research of Zaozhuang University","award":["1020714"],"award-info":[{"award-number":["1020714"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The joint classification of hyperspectral imagery (HSI) and LiDAR data is an important task in the field of remote sensing image interpretation. Traditional classification methods, such as support vector machine (SVM) and random forest (RF), have difficulty capturing the complex spectral\u2013spatial\u2013elevation correlation information. Recently, important progress has been made in HSI-LiDAR classification using Convolutional Neural Networks (CNNs) and Transformers. However, due to the large spatial extent of remote sensing images, the vanilla Transformer and CNNs struggle to effectively capture global context. Moreover, the weak misalignment between multi-source data poses challenges for their effective fusion. In this paper, we introduce AFA\u2013Mamba, an Adaptive Feature Alignment Network with a Global\u2013Local Mamba design that achieves accurate land cover classification. It contains two main core designs: (1) We first propose a Global\u2013Local Mamba encoder, which effectively models context through a 2D selective scanning mechanism while introducing local bias to enhance the spatial features of local objects. (2) We also propose an SSE Adaptive Alignment and Fusion (A2F) module to adaptively adjust the relative positions between multi-source features. This module establishes a guided subspace to accurately estimate feature-level offsets, enabling optimal fusion. As a result, our AFA\u2013Mamba consistently outperforms state-of-the-art multi-source fusion classification approaches across multiple datasets.<\/jats:p>","DOI":"10.3390\/rs16214050","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T09:57:36Z","timestamp":1730368656000},"page":"4050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["AFA\u2013Mamba: Adaptive Feature Alignment with Global\u2013Local Mamba for Hyperspectral and LiDAR Data Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Sai","family":"Li","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang 277160, China"},{"name":"Zaozhuang Robot Autonomous Positioning and Navigation Technology Innovation Center, Zaozhuang 277160, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4581-9332","authenticated-orcid":false,"given":"Shuo","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.arcontrol.2021.03.003","article-title":"A comprehensive review of hyperspectral data fusion with lidar and sar data","volume":"51","author":"Kahraman","year":"2021","journal-title":"Annu. 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