Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification. In this paper, we propose a modified diversity of class probability estimation (MDCPE) with two deep neural networks to learn spectral-spatial feature for HSI classification. In co-training phase, recurrent neural network (RNN) and convolutional neural network (CNN) are utilized as two learners to extract features from labeled and unlabeled data. Based on the extracted features, MDCPE selects most credible samples to update initial labeled data by combining k-means clustering with the traditional diversity of class probability estimation (DCPE) co-training. In this way, MDCPE can keep new labeled data class-balanced and extract discriminative features for both the minority and majority classes. During testing process, classification results are acquired by co-decision of the two learners. Experimental results demonstrate that the proposed semi-supervised co-training method can make full use of unlabeled information to enhance generality of the learners and achieve favorable accuracies on all three widely used data sets: Salinas, Pavia University and Pavia Center.
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
National University of Defense Technology（国防科技大学）；National Innovation Institute of Defense Technology, Chinese Academy of Military Science（军事科学院国防科技创新研究院）
Transformer-Driven Active Transfer Learning for Cross-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）、国王法赫德石油大学）；Engineering Department, University of Sannio（工程系，萨尼奥大学）；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，梅斯辛纳大学）；School of Computer Science, University of Hull（计算机科学学院，霍尔大学）
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence
State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Faculty of Geographical Science and Engineering（空间基准状态关键实验室，遥感与地理信息工程学院，地理科学与工程学院）；School of Computer and Information Engineering（计算机与信息工程学院）；Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University（空间时间大数据工业技术研究院，河南大学）；National Key Laboratory of Integrated Aircraft Control Technology, School of Automation Science and Electrical Engineering, Beihang university（集成飞行器控制技术国家关键实验室，自动化科学与电气工程学院，北航）
Hyperspectral Image Generation with Unmixing Guided Diffusion Model
School of Statistics and Data Science, KLMDASR, LEBPS, and LPMC, Nankai University（统计与数据科学学院，KLMDASR，LEBPS和LPMC，南开大学）；Image Processing Center, School of Astronautics, Beihang University（图像处理中心，航天学院，北航大学）
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；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（格勒诺布尔国立理工学院）；University of Iceland（冰岛大学）；RIKEN（日本理化学研究所）
Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
CNES（法国国家空间研究中心）；Toulouse Mathematics Institute（图卢兹数学研究所）；CNRS（法国国家科学研究中心）；University of Toulouse（图卢兹大学）；ONERA-DOTA（法国航空航天研究院DOTA部门）；Magellium（马哲利姆公司）
针对现有高光谱数据集不适合评估泛化性能的问题，发布大规模Toulouse Hyperspectral Data Set基准，并基于Masked Autoencoder等自监督技术建立像素级分类基线，实现85%准确率。
HSIGene: A Foundation Model For Hyperspectral Image Generation
School of Mathematics and Statistics, Northwestern Polytechnical University（西北工业大学数学与统计学院）；Xidian University（西安电子科技大学）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences（中国科学院成都山地灾害与环境研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Bell Labs（贝尔实验室）；CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences（中国科学院青藏高原地球科学卓越创新中心）；School of Economics and Management, Southwest Jiao Tong University（西南交通大学经济管理学院）
Revisiting Adversarial Training under Hyperspectral Image
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Engineering Research Center of Blockchain Application, Supervision and Management, Ministry of Education, Southeast University（东南大学教育部区块链应用监管工程研究中心）；Purple Mountain Laboratories（紫金山实验室）；Collaborative Innovation Center of Geospatial Technology, Wuhan University（武汉大学地理空间技术协同创新中心）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images
Department of Surgical \& Interventional Engineering, School of Biomedical Engineering \& Imaging Sciences, King's College London, London SE1 7EH Department of Neurosurgery, King's College London Hospital NHS Foundation Trust, London SE5 9RS, UK. Department of Imaging Chemistry \& Biology, School of Biomedical Engineering \& Imaging Sciences, King's College London, London SE1 7EH, UK.
Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach
FOSS Analytical A/S（福斯分析公司）；Department of Computer Science (DIKU), University of Copenhagen（哥本哈根大学计算机科学系）；Department of Food Science (UCPH FOOD), University of Copenhagen（哥本哈根大学食品科学系）；Information Technology Group, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心信息技术组）；Food and Biobased Research, Wageningen University and Research (WUR)（瓦赫宁根大学及研究中心食品与生物基研究）；Natural History Museum of Denmark (NHMD), University of Copenhagen（哥本哈根大学丹麦自然历史博物馆）
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）
