As the ground objects become increasingly complex, the classification results obtained by single source remote sensing data can hardly meet the application requirements. In order to tackle this limitation, we propose a simple yet effective attention fusion model based on Disentangled Non-local (DNL) network for hyperspectral and LiDAR data joint classification task. In this model, according to the spectral and spatial characteristics of HSI and LiDAR, a multiscale module and a convolutional neural network (CNN) are used to capture the spectral and spatial characteristics respectively. In addition, the extracted HSI and LiDAR features are fused through some operations to obtain the feature information more in line with the real situation. Finally, the above three data are fed into different branches of the DNL module, respectively. Extensive experiments on Houston dataset show that the proposed network is superior and more effective compared to several of the most advanced baselines in HSI and LiDAR joint classification missions.
LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；Queensland Department of Agriculture and Fisheries（昆士兰州农业与渔业厅）
Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；Mississippi State University（密西西比州立大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Nanjing University of Information Science and Technology（南京信息工程大学）；University of Missouri-Kansas City（密苏里大学堪萨斯城分校）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；Helmholtz Institute Freiberg for Resource Technology（弗赖贝格资源技术亥姆霍兹研究所）；German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
University of Electronic Science and Technology of China（电子科技大学）；Oregon State University（俄勒冈州立大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
Department of Forest Sciences, University of Helsinki（赫尔辛基大学森林科学系）；School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（武汉大学计算机学院、国家多媒体软件工程研究中心、人工智能研究院、湖北省多媒体与网络通信工程重点实验室，武汉，中国）
Multimodal Fusion Transformer for Remote Sensing Image Classification
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Technical University of Munich（慕尼黑工业大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Extremadura（埃斯特雷马杜拉大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
