Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. HSI contains much more information than traditional visual images for identifying the categories of land covers. One recent feasible solution for HSI is to leverage CapsNets for capturing spectral-spatial information. However, these methods require high computational requirements due to the full connection architecture between stacked capsule layers. To solve this problem, a DWT-CapsNet is proposed to identify partial but important connections in CapsNet for a effective and efficient HSI classification. Specifically, we integrate a tailored attention mechanism into a Discrete Wavelet Transform (DWT)-based downsampling layer, alleviating the information loss problem of conventional downsampling operation in feature extractors. Moreover, we propose a novel multi-scale routing algorithm that prunes a large proportion of connections in CapsNet. A capsule pyramid fusion mechanism is designed to aggregate the spectral-spatial relationships in multiple levels of granularity, and then a self-attention mechanism is further conducted in a partially and locally connected architecture to emphasize the meaningful relationships. As shown in the experimental results, our method achieves state-of-the-art accuracy while keeping lower computational demand regarding running time, flops, and the number of parameters, rendering it an appealing choice for practical implementation in HSI classification.
1D-Convolutional Capsule Network for Hyperspectral Image Classification
Liaoning Technical University（辽宁工程技术大学）；Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（中国科学院福建物质结构研究所）；East China Normal University（华东师范大学）；The Hong Kong Polytechnic University（香港理工大学）
SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing
Info Edge India Ltd；Department of Artificial Intelligence and Machine Learning（人工智能与机器学习系）；Acharya Institute of Technology（阿查里亚理工学院）；Indian Institute of Information Technology Vadodara (IIITV)（瓦多拉印度信息科技学院）
Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations
National Institute of Technology Puducherry（本地治里国家技术学院）；Sardar Vallabhbhai National Institute of Technology Surat（苏拉特萨达尔·瓦拉巴伊国家技术学院）；Norwegian University of Science and Technology（挪威科技大学）
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China（教育部智能感知与图像理解重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Xidian University（西安电子科技大学）；School of Artificial Intelligence（人工智能学院）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；School of Electronic Engineering（电子工程学院）
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
The University of Sydney（悉尼大学）；The Chinese University of Hong Kong (CUHK)（香港中文大学）；Northeastern University（东北大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）
Optimal Hyperspectral Undersampling Strategy for Satellite Imaging
Bauman Moscow State Technical University（巴乌曼莫斯科国立技术大学）；VeinCV LLC（VeinCV公司）；Moscow Technical University of Communications and Informatics（莫斯科通信与信息学技术大学）；GIS（2GIS公司）；Artificial Intelligence Research Institute（人工智能研究 institutes）
Iterative Low-rank Network for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）；College of Computer Science, Zhejiang University（计算机科学学院，浙江大学）
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）
A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery
Manchester Metropolitan University（曼彻斯特城市大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Brunel University（布鲁内尔大学）
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）
