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）
Recently, large multimodal models have built a bridge from visual to textual information, but they tend to underperform in remote sensing scenarios. This underperformance is due to the complex distribution of objects and the significant scale differences among targets in remote sensing images, leading to visual ambiguities and insufficient descriptions by these multimodal models. Moreover, the lack of multimodal fine-tuning data specific to the remote sensing field makes it challenging for the model's behavior to align with user queries. To address these issues, this paper proposes an attribute-guided \textbf{Multi-Granularity Instruction Multimodal Model (MGIMM)} for remote sensing image detailed description. MGIMM guides the multimodal model to learn the consistency between visual regions and corresponding text attributes (such as object names, colors, and shapes) through region-level instruction tuning. Then, with the multimodal model aligned on region-attribute, guided by multi-grain visual features, MGIMM fully perceives both region-level and global image information, utilizing large language models for comprehensive descriptions of remote sensing images. Due to the lack of a standard benchmark for generating detailed descriptions of remote sensing images, we construct a dataset featuring 38,320 region-attribute pairs and 23,463 image-detailed description pairs. Compared with various advanced methods on this dataset, the results demonstrate the effectiveness of MGIMM's region-attribute guided learning approach. Code can be available at https://github.com/yangcong356/MGIMM.git
DGTRSD & DGTRS-CLIP: A Dual-Granularity Remote Sensing Image-Text Dataset and Vision Language Foundation Model for Alignment
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院 aerospace information research institute）；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 Information Network Security, People’s Public Security University of China（中国人民公安大学信息网络安全学院）
Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main characters: large intra-class variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two characters. We propose a Multi-granularity Multi-Level Feature Ensemble Network (MGML-FENet) to efficiently tackle RS scene classification task in this paper. Specifically, we propose Multi-granularity Multi-Level Feature Fusion Branch (MGML-FFB) to extract multi-granularity feature…
Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM
Research Center for Space Computing System, Zhejiang Lab（浙江省实验室空间计算系统研究中心）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Cyber Science and Engineering, Zhengzhou University（郑州大学网络科学与工程学院）；Liaoning University of Technology（辽宁科技学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University（武汉测绘遥感与信息工程国家重点实验室（LIESMARS））；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data
School of Earth and Space Sciences, Peking University（地球与空间科学学院，北京大学）；College of Urban and Environmental Sciences, Peking University（城市与环境科学学院，北京大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, CAS（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；Intelligent Maintenance and Operations Systems Lab（智能维护与运营系统实验室）
Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey
Department of Electronic and Information Engineering, Beihang University（北航电子与信息工程学院）；Image Processing Center, School of Astronautics, Beihang University（北航航天学院图像处理中心）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）
University of East Anglia（东英吉利大学）；Newcastle University（纽卡斯尔大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Recognising remote sensing scene images remains challenging due to large visual-semantic discrepancies. These mainly arise due to the lack of detailed annotations that can be employed to align pixel-level representations with high-level semantic labels. As the tagging process is labour-intensive and subjective, we hereby propose a novel Multi-Granularity Canonical Appearance Pooling (MG-CAP) to automatically capture the latent ontological structure of remote sensing datasets. We design a granular framework that allows progressively cropping the input image to learn multi-grained features. For …
Beijing Institute of Technology（北京理工大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Hubei University of Economics（湖北经济学院）；Luoyang Institute of Electro-Optical Equipment, Aviation Industry Corporation of China, Ltd.（中国航空工业集团有限公司洛阳电光设备研究所）
Arbitrary-oriented object detection (AOOD) plays a significant role for image understanding in remote sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multi-grained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to ref…
Image-Conditioned Instance Prompt Network for Referring Remote Sensing Image Segmentation
School of Computer Science, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机科学学院，西安，中国）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China（复旦大学智能机器人与先进制造学院，上海，中国）；Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen, China（西北工业大学深圳研究院，深圳，中国）
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing
Institute of Space Earth Science, School of Frontier Sciences, Nanjing University（南京大学空间地球科学学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所（CIRES））；Institute of Behavioral Science（行为科学研究所）
Despite abundant data on the spatial distribution of contemporary human settlements, historical data on the long-term evolution of human settlements at fine spatial and temporal granularity is scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used mapping methods (e.g., image classification) and suitable data sources (i.e., aerial imagery, multi-spectral remote sensing data, LiDAR) have only been available in recent decades. However, there are alternative data sources such as cadastral records that are digitally available, containi…
Beyond Global Scanning: Adaptive Visual State Space Modeling for Salient Object Detection in Optical Remote Sensing Images
School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
GeoRoPE: Ground-Aware Rotary Adaptation for Remote Sensing Foundation Models
The University of Sydney（悉尼大学）；Edith Cowan University（埃迪斯科文大学）；Adelaide University（阿德莱德大学）；Wuhan Polytechnic University（武汉轻工大学）；Climate, Orange Agricultural Institute（气候研究所，奥兰治农业研究所）；La Trobe University（拉筹伯大学）
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Nanyang Technological University（南洋理工大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics（上海财经大学经济与计算交叉研究重点实验室）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通讯研究院）
LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection
Shen Yuan Honors College, Beihang University（北京航空航天大学沈元荣誉学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology（中国空间技术研究院钱学森空间技术实验室）
MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；National Key Laboratory for Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
UniRSCD: A Unified Novel Architectural Paradigm for Remote Sensing Change Detection
College of Artificial Intelligence, Southwest University（西南大学人工智能学院）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）；LIESMARS, Wuhan University（武汉大学LIESMARS）；CS, Chongqing University（重庆大学计算机学院）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学多学科先进研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）
P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation
School of Information Science and Technology, Northwest University（西北大学信息科学与技术学院）；School of Art and Media, Beijing Normal University（北京师范大学艺术与传媒学院）；School of Management, Nanjing Police University（南京警察学院管理学院）
From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation
College of Science and Engineering and Centre for AI and Data Science Innovation, James Cook University（科学与工程学院和人工智能与数据科学创新中心，詹姆斯库克大学）；Department of Forest and Wildlife Ecology, University of Wisconsin-Madison（森林与野生动物生态学系，威斯康星大学麦迪逊分校）；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）
Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
University of Liverpool（利物浦大学）；Beihang University（北京航空航天大学）；Cambridge University（剑桥大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Tsinghua University（清华大学）
Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas. Most existing works pay attention to the holistic impression of medical images, failing to utilize important anatomy information. However, in actual clinical practice, radiologists usually locate important anatomical structures, and then look for signs of abnormalities in certain structures and reason the underlying disease. In this paper, we propose a novel framework AGFNet…
Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP), Beijing Institute of Technology（北京理工大学空间智能信息处理国家重点实验室）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统技术重点实验室）；Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学前沿交叉科学研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电学院）；School of Earth and Space Sciences, Peking University（北京大学地球与空间科学学院）；School of Electronics, Peking University（北京大学电子学院）；School of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing（华中科技大学计算机科学与技术学院&湖北省智能地理信息处理重点实验室）
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
