Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba architecture to enhance HSI classification. WaveMamba captures both local texture patterns and global contextual relationships in an end-to-end trainable model. The Wavelet-based enhanced features are then processed through the state-space architecture to model spatial-spectral relationships and temporal dependencies. The experimental results indicate that WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5\% on the University of Houston dataset and a 2.0\% increase on the Pavia University dataset.
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Guangzhou University（广州大学）；University of Macau（澳门大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification
Nanjing University of Information Science and Technology（南京信息工程大学）；Institute of Optics and Electronics（光学电子研究所）；China University of Geosciences（中国地质大学）；University of Extremadura（埃斯特雷马杜拉大学）
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification
Nanjing University of Information Science and Technology（南京信息工程大学）；Institute of Optics and Electronics（光学电子研究所）；China University of Geosciences（中国地质大学）；University of Extremadura（埃斯特雷马杜拉大学）
HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification
College of Mathematics and Informatics, South China Agricultural University（华南农业大学数学与信息学院）；Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs（热带南方智能农业技术重点实验室，农业部）；School of Chemistry and Chemical Engineering, South China University of Technology（华南理工大学化学与化工学院）
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
University of Messina（墨西拿大学）；Shaoxing University（绍兴大学）；University of Hull（赫尔大学）；Innopolis University（因诺波利斯大学）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；Univ. Grenoble Alpes（格勒诺布尔大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification
Graduate School of Information, Production and Systems, Waseda University（早稻田大学信息、生产与系统研究科）；Key Laboratory of Electromagnetic Waves (EMW) Information, Fudan University（复旦大学电磁波信息重点实验室）；Hong Kong Polytechnic University（香港理工大学）
MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Urban Design, Wuhan University（武汉大学城市设计学院）；Aerospace Information Research Institute, Henan Academy of Sciences（河南科学院航天信息研究所）
MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Computer Science（计算机学院）；Institute of Artificial Intelligence（人工智能研究院）；Hubei Key Laboratory of Multimedia and Network Communication Engineering（湖北省多媒体与网络通信工程重点实验室）；Wuhan University（武汉大学）
Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；Shaoxing University（绍兴大学）；King Saud University（国王 Saud 大学）；Innopolis University（Innopolis 大学）；University of Messina（墨西拿大学）
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能教育部重点实验室、人工智能研究院）
Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification
University of Messina（墨西拿大学）；Shaoxing University（绍兴大学）；National University of Computer and Emerging Sciences (NUCES)（国家计算机与新兴科学大学）；Innopolis University（因诺波利斯大学）；University of Hull（赫尔大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）
VP-Hype: A Hybrid Mamba-Transformer Framework with Visual-Textual Prompting for Hyperspectral Image Classification
Institute of Applied Sciences and Intelligent Systems (ISASI), CNR（应用科学与智能系统研究所（ISASI），CNR）；Dept. of Engineering for Innovation, University of Salento（创新工程系，萨勒诺大学）；VSC Laboratory, Department of Electronics and Automation, University of Biskra（VSC实验室，电子与自动化系，比斯克拉大学）；Dept. of Computer Science and AI, University of the Basque Country (UPV/EHU)（计算机科学与人工智能系，巴斯克国家大学（UPV/EHU））；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi（索邦大学阿布扎克人工智能中心）
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Peng Cheng Laboratory（鹏城实验室）；School of Information Engineering, Zhongnan University of Economics and Law（中南财经政法大学信息工程学院）；Emergency Management Research Center, Zhongnan University of Economics and Law（中南财经政法大学应急管理研究中心）
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；School of Mathematics and Statistics, Southeast University（东南大学数学学院）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、约瑟夫·傅里叶大学）
Cosine-Normalized Attention for Hyperspectral Image Classification
SDAIA-KFUPM, Joint Research Center for Artificial Intelligence (JRCAI), King Fahd University of Petroleum and Minerals（SDAIA-KFUPM联合人工智能研究中心（JRCAI），法赫德国王石油矿产大学）；Institute of Software Development and Engineering, Innopolis University（软件开发和工程学院，因诺波利斯大学）
EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
Department of Computer Science, National University of Computer and Emerging Sciences（计算机科学系，国立计算机与新兴科学大学）；Institute of Software Development and Engineering, Innopolis University（软件开发与工程研究所，因诺波利斯大学）；Dipartimento di Matematica e Informatica—MIFT, University of Messina（数学与信息学系—MIFT，墨西拿大学）
LGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学信息中心（广州））；Faculty of Geographical Science, Beijing Normal University（北京师范大学地理学部）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）
Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Hunan University（湖南大学）
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Guangzhou University（广州大学）
BCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
College of Information Science, University of Arizona（亚利桑那大学信息科学学院）；Department of Electrical and Computer Engineering, University of Arizona（亚利桑那大学电气与计算机工程系）
Hyperspectral Mamba for Hyperspectral Object Tracking
State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University（信息服务网络国家重点实验室，电信工程学院，西安电子科技大学）；The Department of Electronic and Electrical Engineering, the University of Sheffield（电子与电气工程系，谢菲尔德大学）
Physics-Aligned Spectral Mamba: Decoupling Semantics and Dynamics for Few-Shot Hyperspectral Target Detection
Research Center for Space Computing System, Zhejiang Lab（浙江实验室空间计算系统研究中心）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model
College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；National Subsea Centre, Robert Gordon University（罗伯特戈登大学国家海底中心）；Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心亥姆霍兹弗赖贝格资源技术研究所）
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
该研究提出 SSUMamba，通过六种空间-光谱连续扫描顺序、双向 SSM 与 3D 卷积高效建模高光谱图像的长程及局部依赖，在降低每批内存消耗的同时提升去噪性能。
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；Aerospace Information Research Institute, Henan Academy of Sciences（航天信息研究所，河南科学院）
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
Department of Computer Science, National University of Computer and Emerging Sciences（计算机科学系，国立计算机与新兴科学大学）；Dipartimento di Matematica e Informatica-MIFT, University of Messina（数学与信息学系-MIFT，墨西拿大学）；School of Computer Science, University of Hull（计算机科学学院，赫尔大学）；Institute of Software Development and Engineering, Innopolis University（软件开发与工程研究所，因诺波利斯大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（航空信息研究所，中国科学院）；Big Geospatial Data Management, Technical University of Munich（大规模地理空间数据管理，慕尼黑技术大学）；Department of Infrastructure Engineering, University of Melbourne（基础设施工程系，墨尔本大学）；Intelligent Control Laboratory, PLA Rocket Force University of Engineering（智能控制实验室，中国人民解放军火箭军工程大学）；Institute of Methodologies for Environmental Analysis, National Research Council（环境分析方法研究所，国家研究理事会）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（电子、电气与通信工程学院，中国科学院大学）
FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；School of Mechanical Engineering, University of Science and Technology Beijing（北京科技大学机械工程学院）；School of Instrument and Electronics, North University of China（北方大学仪器与电子学院）
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging
University of Helsinki（赫尔辛基大学）；IMEC（微电子中心）；Universiteit Gent（根特大学）；Northwestern Polytechnical University（西北工业大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；school of software（软件学院）
A PyTorch Library for Hyperspectral Image Models: Technical Report
Siddhartha Academy of Higher Education（悉达多高等教育学院）；Vellore Institute of Technology（韦洛尔理工学院）；Indira Gandhi National Open University（英迪拉·甘地国立开放大学）；Tezpur University（提斯浦尔大学）
WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion
School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（计算、工程与数学科学学院，拉特罗布大学）；CSIRO Agriculture and Food（澳大利亚农业与食品CSIRO）
