DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
Min Sun School of Materials Science and Engineering Hainan University（孙敏 海南大学材料科学与工程学院）；Fenghui Guo School of Materials Science and Engineering Hainan University（郭峰 海南大学材料科学与工程学院）
遥感变化检测（RSCD）对于识别土地覆盖变化至关重要，然而现有方法，包括最先进的State Space Models（SSMs），通常缺乏处理几何失配的显式机制，并且难以区分细微的真实变化与噪声。为了解决这一问题，我们提出了DC-Mamba，一个基于ChangeMamba骨干网络构建的“先对齐后增强”框架。该框架集成了两个轻量级、即插即用的模块：（1）Bi-Temporal Deformable Alignment（BTDA），在语义特征层面显式引入几何感知以校正空间失配；（2）Scale-Sparse Change Amplifier（SSCA），利用多源线索在最终分类前选择性地放大高置信度变化信号，同时抑制噪声。这种协同设计首先通过BTDA建立几何一致性以减少伪变化，然后利用SSCA锐化边界并增强细小或微弱目标的可视性。实验表明，我们的方法在强大的ChangeMamba基线上显著提升了性能，F1-score从0.5730提高到0.5903，IoU从0.4015提高到0.4187。结果证实了“先对齐后增强”策略的有效性，为RSCD中几何层面和特征层面的挑战提供了一种鲁棒且易于部署的解决方案。
英文摘要：
Remote sensing change detection (RSCD) is vital for identifying land-cover changes, yet existing methods, including state-of-the-art State Space Models (SSMs), often lack explicit mechanisms to handle geometric misalignments and struggle to distinguish subtle, true changes from noise.To address this, we introduce DC-Mamba, an "align-then-enhance" framework built upon the ChangeMamba backbone. It integrates two lightweight, plug-and-play modules: (1) Bi-Temporal Deformable Alignment (BTDA), which explicitly introduces geometric awareness to correct spatial misalignments at the semantic feature level; and (2) a Scale-Sparse Change Amplifier(SSCA), which uses multi-source cues to selectively amplify high-confidence change signals while suppressing noise before the final classification. This synergistic design first establishes geometric consistency with BTDA to reduce pseudo-changes, then leverages SSCA to sharpen boundaries and enhance the visibility of small or subtle targets. Experiments show our method significantly improves performance over the strong ChangeMamba baseline, increasing the F1-score from 0.5730 to 0.5903 and IoU from 0.4015 to 0.4187. The results confirm the effectiveness of our "align-then-enhance" strategy, offering a robust and easily deployable solution that transparently addresses both geometric and feature-level challenges in RSCD.
Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
Department of Physics, J.K. Institute of Science（J.K.科学研究所物理系）；World Scientific University（世界科学出版社大学）；University of Intelligent Studies（智能研究大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）
RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（计算机科学学院，北京邮电大学）；School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China（电子工程学院，北京邮电大学）
Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北航电子信息工程学院）；Department of Paediatrics, Cambridge University（剑桥大学儿科部）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Department of Computing Science, Robert Gordon University（罗伯特·戈登大学计算机科学系）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家实验室）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
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 Target Cognition and Application Technology, CAS（中国科学院目标认知与应用技术重点实验室）；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）
SwinMamba: A hybrid local-global mamba framework for enhancing semantic segmentation of remotely sensed images
Xi'an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Nanyang Technological University（南洋理工大学）；SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology（上海交通大学机械与动力工程学院-卡奥斯新一代工业智能技术联合研究中心）
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Space Mechanics and Electricity（北京空间机电研究所）；Donghai Laboratory（东海实验室）
Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes
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）；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）
Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection
Georgia Institute of Technology（佐治亚理工学院）；Indian Institute of Engineering Science and Technology (IIEST), Shibpur（印度工程科学与技术学院（IIEST），希布普尔）；Nanyang Technological University（南洋理工大学）；Indian Institute of Technology, Delhi（印度理工学院德里分校）；Duke University（杜克大学）
2DMCG:2DMambawith Change Flow Guidance for Change Detection in Remote Sensing
Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Jiangnan University（江南大学教育部智慧医疗技术工程研究中心）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
College of Computer Science and Technology, National University of Defense Technology, China（国防科技大学计算机科学与技术学院）；Tsinghua University, China（清华大学）；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）
RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation
the Future Network of Intelligence Institute (FNii), the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）未来网络智能研究院）；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）
The University of Tokyo（东京大学）；Wuhan University（武汉大学）；RIKEN（理化学研究所）；Microsoft Research Asia（微软亚洲研究院）
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for …
OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
Department of Computer Science, University of Exeter（计算机科学系，埃克塞特大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））
UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection
College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Image Processing Center, Beihang University, Beijing, China（北京航空航天大学图像处理中心）；Robotics Institute, Beihang University, Beijing, China（北京航空航天大学机器人研究所）；Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China（华中科技大学同济医院）
CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model
School of Software Technology, Zhejiang University（浙江大学软件学院）；Noah’s Ark Lab, Huawei（华为诺亚方舟实验室）；School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Exeter（埃克塞特大学）
Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals
Department of Computer Engineering, Universidad de Burgos（巴尔多大学计算机工程系）；Biodiversity Research Institute, Spanish Research Council, University of Oviedo, Principado de Asturias（生物多样性研究机构、西班牙研究委员会、奥维多大学、阿斯图里亚斯公国）；School of Computer Science and Electronic Engineering, Bangor university（班戈大学计算机科学与电子工程学院）
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）
Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection
National Space Science Center, Chinese Academy of Sciences（中国科学院国家空间科学中心）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术系）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学Queen Mary电子工程与计算机科学学院）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学系）
3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection
School of Computer Science and Technology, Civil Aviation University of China（计算机科学与技术学院，中国民航大学）；School of Safety Science and Engineering, Civil Aviation University of China（安全科学与工程学院，中国民航大学）
COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；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）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学院）；School of Geophysics and Geomatics, China University of Geoscience（中国地质大学（武汉）地球物理与空间信息学院）
Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark
Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院整形外科医院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Nanjing University of Science and Technology（南京理工大学）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）
3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
School of Electronic and Optical Engineering（电子与光学工程学院）；The 28th Research Institute of China Electronics Technology Group Corporation（中国电子科技集团第二十八研究所）；College of Astronautics（航天学院）；School of Information and Communication Engineering（信息与通信工程学院）；State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument（极端环境光电动态测量技术与仪器国家重点实验室）
UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images
School of Computer Technology and Applications, Qinghai University（青海大学计算机技术与应用学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
