深度学习技术在遥感图像语义分割和土地利用变化检测中取得了显著成功。然而，其在边缘平台上的实时部署仍受到解码器复杂度的限制。为此，我们引入了LightFormer，一种用于涉及无结构目标的时间敏感任务的轻量级解码器，例如灾害评估、无人机搜救和文化遗产监测。LightFormer采用基于通道处理和可学习门控机制的特征融合与细化模块，高效地聚合多尺度、多范围信息，从而大幅减少模型复杂度。此外，我们提出了一种空间信息选择模块（SISM），整合了长程注意力与细节保留分支，以捕捉多尺度下的空间依赖性，从而显著提高复杂场景中无结构目标的识别能力。在ISPRS Vaihingen基准测试中，LightFormer在保持GLFFNet的mIoU（83.9% vs. 84.0%）的同时，仅需其14.7%的FLOPs和15.9%的参数，从而实现了出色的精度-效率权衡。在LoveDA、ISPRS Potsdam、RescueNet和FloodNet上的一致结果进一步证明了其鲁棒性和对无结构物体的优越感知能力。这些发现凸显了LightFormer作为遥感应用中既需计算经济性又需高精度分割的实用解决方案。
英文摘要：
Deep learning techniques have achieved remarkable success in the semantic segmentation of remote sensing images and in land-use change detection. Nevertheless, their real-time deployment on edge platforms remains constrained by decoder complexity. Herein, we introduce LightFormer, a lightweight decoder for time-critical tasks that involve unstructured targets, such as disaster assessment, unmanned aerial vehicle search-and-rescue, and cultural heritage monitoring. LightFormer employs a feature-fusion and refinement module built on channel processing and a learnable gating mechanism to aggregate multi-scale, multi-range information efficiently, which drastically curtails model complexity. Furthermore, we propose a spatial information selection module (SISM) that integrates long-range attention with a detail preservation branch to capture spatial dependencies across multiple scales, thereby substantially improving the recognition of unstructured targets in complex scenes. On the ISPRS Vaihingen benchmark, LightFormer attains 99.9% of GLFFNet's mIoU (83.9% vs. 84.0%) while requiring only 14.7% of its FLOPs and 15.9% of its parameters, thus achieving an excellent accuracy-efficiency trade-off. Consistent results on LoveDA, ISPRS Potsdam, RescueNet, and FloodNet further demonstrate its robustness and superior perception of unstructured objects. These findings highlight LightFormer as a practical solution for remote sensing applications where both computational economy and high-precision segmentation are imperative.
Wuhan University（武汉大学）；Lancaster University（兰卡斯特大学）；UK Centre for Ecology & Hydrology（英国生态与水文中心）
The fully convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an encoder to capture multilevel feature maps, which are incorporated into the final prediction by a decoder. As the context is crucial for precise segmentation, tremendous effort has been made to extract such information in an intelligent fashion, including employing dilated/atrous convolutions or inserting attention modules. However, these endeavors are all based on the FCN architecture with ResNet or other backbones, w…
Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this paper, we propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and …
With the advancement of remote-sensed imaging large volumes of very high resolution land cover images can now be obtained. Automation of object recognition in these 2D images, however, is still a key issue. High intra-class variance and low inter-class variance in Very High Resolution (VHR) images hamper the accuracy of prediction in object recognition tasks. Most successful techniques in various computer vision tasks recently are based on deep supervised learning. In this work, a deep Convolutional Neural Network (CNN) based on symmetric encoder-decoder architecture with skip connections is e…
New York University（纽约大学）；NYU Abu Dhabi（纽约大学阿布扎比分校）
Recent studies have shown the benefits of using additional elevation data (e.g., DSM) for enhancing the performance of the semantic segmentation of aerial images. However, previous methods mostly adopt 3D elevation information as additional inputs. While in many real-world applications, one does not have the corresponding DSM information at hand and the spatial resolution of acquired DSM images usually do not match the aerial images. To alleviate this data constraint and also take advantage of 3D elevation information, in this paper, we introduce a geometry-aware segmentation model that achiev…
Kolmogorov-Arnold Network for Remote Sensing Image Semantic Segmentation
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；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）
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）
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）
Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model
School of Mathematical Sciences, Beijing Normal University（北京师范大学数学科学学院）；State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University（北京师范大学地理科学学部遥感科学国家重点实验室）
Shenzhen Institutes of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；Beijing Futong Dongfang Technology Co, Ltd（北京富通东方科技有限公司）；Beijing University of Chemical Technology（北京化工大学）
Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoder-decoder network and the SELayer based fusion and assembling techniques, the prop…
Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（武汉大学计算机学院国家多媒体软件工程技术研究中心）；Hubei Luojia Laboratory（湖北珞珈实验室）；School of Urban Design, Wuhan University（武汉大学城市设计学院）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
City University of Hong Kong（香港城市大学）；Beijing Jiaotong University（北京交通大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Tianjin University（天津大学）
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI. In this paper, we propose an end-to-end deep network called LV-Net based on the shape of network architecture, which detects salient objects from optical RSIs in a purely data-driven fashion. The proposed LV-Net consists of two key modules, i.e., a two-stream pyramid module (L-shaped module) and an encoder-decoder module with nested co…
Wuhan University（武汉大学）；Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources（自然资源部华南热带亚热带自然资源监测重点实验室）；Lancaster University（兰卡斯特大学）；UK Centre for Ecology & Hydrology（英国生态与水文研究中心）；University of Twente（特文特大学）；University of Southampton（南安普顿大学）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）
Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications, such as land cover mapping, urban change detection, environmental protection, and economic assessment.Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has dominated semantic segmentation for many years. CNN adopts hierarchical feature representation, demonstrating strong capabilities for local information extraction. However, the local property of the convolution layer limits the network from capturing the global context. Recently,…
LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images
School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；School of Health Sciences and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院）；Institute of Seismology, China Earthquake Administration（中国地震局地震研究所）
MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Helmholtz Institute Freiberg for Resource Technology（弗莱贝格亥姆霍兹资源技术研究所）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；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）；School of Engineering, Shiv Nadar University（希夫·纳达尔大学工程学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Lancaster University（兰卡斯特大学）
Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method named ScRoadExtractor, which learns from easily accessible scribbles such as centerlines instead of densely annotated road surface ground-truths. To propagate semantic information from sparse scribbles to unlabeled pixels, we introduce a r…
A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）；Nanjing Center, China Geological Survey（中国地质调查局南京中心）；School of Automation, Southeast University（东南大学自动化学院）
Siamese networks are widely used for remote sensing change detection tasks. A vanilla siamese network has two identical feature extraction branches which share weights, these two branches work independently and the feature maps are not fused until about to be sent to a decoder head. However we find that it is critical to exchange information between two feature extraction branches at early stage for change detection task. In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature…
Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学上海先进通信与数据科学研究院）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Computer Science, Stony Brook University（石溪大学计算机科学系）
In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the…
Lancaster University（兰卡斯特大学）；UK Centre for Ecology & Hydrology（英国生态与水文中心）；Wuhan University（武汉大学）；University of Southampton（南安普顿大学）
Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign s…
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）
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））
ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery
1 Inst.\ of Data Science, National Cheng Kung University, \ , Taiwan - 2 Inst.\ of Intelligent Systems College of Artificial Intelligence, \ Yang Ming Chiao Tung University, Hsinchu, Taiwan
Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation
School of Information Science and Technology, Yunnan Normal University（云南师范大学信息科学与技术学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器电气工程学院）；Key Laboratory of Geophysical Exploration Equipment, Ministry of Education（教育部地球物理探测装备重点实验室）；School of Mathematics, Jilin University（吉林大学数学学院）
