We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor and multiangular images is available. In these situations, images should ideally be spatially coregistred, corrected and compensated for differences in the image domains. Such procedures require the interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps and foldings of the image distributions (or manifolds). The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds, and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike, and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains.
MGIMM: Multi-Granularity Instruction Multimodal Model for Attribute-Guided Remote Sensing Image Detailed Description
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, Wuhan University（武汉大学计算机学院）；Hubei Luojia Laboratory（湖北珞珈实验室）
Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Intelligent Game and Decision Lab (IGDL)（智能游戏与决策实验室）；Yuelushan Center for Industrial Innovation（岳麓山工业创新中心）
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing
Center for the Image Analysis (OGAM) and Department of Electrical and Electronics Engineering of Middle East Technical University (METU)（图像分析中心（OGAM）和中欧技术大学（METU）电子与电气工程系）
A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；Department of Geography, The University of Hong Kong（香港大学地理系）
Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification
Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Key Laboratory of Collaborative Intelligent Systems of Ministry of Education, Xidian University（西安电子科技大学教育部协同智能系统重点实验室）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Academy of Artificial Intelligence, Inner Mongolia Normal University（内蒙古师范大学人工智能学院）
Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；School of Computer Science, Chongqing University, China（重庆大学计算机学院）
LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
State Key Laboratory of information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Electronic Information School, Wuhan University（武汉大学电子信息学院）
Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；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）；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（格勒诺布尔理工学院）
GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
RIKEN AIP（日本理化学研究所革新智能研究中心）；Wuhan University（武汉大学）；Linköping University（林雪平大学）；University of Tokyo（东京大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
SkyNative: A Native Multimodal Architecture for Remote Sensing Vision-Language Understanding
College of Computer Science and Technology, Jilin University, China（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）
SAMChat: Introducing Chain of Thought Reasoning and GRPO to a Multimodal Small Language Model for Small Scale Remote Sensing
Center for the Image Analysis (OGAM) and Department of Electrical and Electronics Engineering of Middle East Technical University (METU)（图像分析中心（OGAM）和中东部技术大学（METU）电子与电气工程系）
A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification
Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology（北京理工大学分数域信号与系统北京市重点实验室）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Key Laboratory of Collaborative Intelligent Systems of Ministry of Education, Xidian University（西安电子科技大学教育部协同智能系统重点实验室）；Academy of Artificial Intelligence, Inner Mongolia Normal University（内蒙古师范大学人工智能研究院）
A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients
Southwest Jiaotong University（西南交通大学）；Beijing Institute of Technology（北京理工学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Cross-modal Context-aware Learning for Visual Prompt Guided Multimodal Image Understanding in Remote Sensing
College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人学院及机器人视觉感知与控制技术国家工程研究中心，湖南大学）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算与人工智能学院，西南交通大学）
ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing
School of Computer Science, Wuhan University（武汉大学计算机学院）；Zhongguancun Academy（中关村科学院）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology（北京理工大学天基智能信息处理科学与技术全国重点实验室）
SVC 2026: the Second Multimodal Deception Detection Challenge and the First Domain Generalized Remote Physiological Measurement Challenge
Wuhan University（武汉大学）；Great Bay University（大湾区大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Hefei University of Technology（合肥工业大学）；University of Barcelona（巴塞罗那大学）
FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensing
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University（电磁波信息科学重点实验室（MoE），复旦大学）；Institute of Zhejiang Laboratory（浙江实验室研究院）；College of Electronic Science and Technology, NUDT（电子科学与技术学院，南大学）
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
College of Computer Science and Technology, National University of Defense Technology, China（中国国防科技大学计算机科学与技术学院）；Beijing University of Posts and Telecommunications, China（北京邮电大学）；School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；Tsinghua University, China（清华大学）；Beihang University, China（北航大学）
XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Zhongguancun Academy（中关村学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
