Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios. Such prior knowledge can be useful because remote sensing objects may be mistakenly recognized without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes a lightweight Large Selective Kernel Network (LSKNet) backbone. LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing images. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard remote sensing classification, object detection and semantic segmentation benchmarks. Our comprehensive analysis further validated the significance of the identified priors and the effectiveness of LSKNet. The code is available at https://github.com/zcablii/LSKNet.
Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, and the long-range context required by different types of objects can vary. In this paper, we take these priors into account and propose the Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial re…
UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects
Easy Lab, College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China（易实验室，数学与信息学院，华南农业大学，广州，中国）；School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院，东南大学，南京，中国）；School of Mathematics, Foshan University, Foshan, China（数学学院，佛山大学，佛山，中国）
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…
Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models
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）
PKINet-v2: Towards Powerful and Efficient Poly-Kernel Remote Sensing Object Detection
Nanjing University of Science and Technology（南京理工大学）；Zhejiang University（浙江大学）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）；State Key Lab. of Intelligent Manufacturing of Advanced Construction Machinery（先进施工机械智能制造国家重点实验室）
YOLO-SPCI: Enhancing Remote Sensing Object Detection via Selective-Perspective-Class Integration
Faculty of Innovation Engineering, Macau University of Science and Technology（澳门科技大学创新工程学院）；School of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；College of Automation and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学自动化与信息工程学院）
MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery
Technical University of Munich（慕尼黑工业大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹研究中心）；Wuhan University of Science and Technology（武汉科技大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Lancaster University（兰卡斯特大学）
With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we designed the FPA module, which links the feature maps of diff…
RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（云南省智能系统与计算重点实验室）
PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；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地理信息工程学院）；Guangxi Water & Power Design Institute CO., Ltd.（广西水电设计院有限公司）
Deep learning based change detection methods have received wide attentoion, thanks to their strong capability in obtaining rich features from images. However, existing AI-based CD methods largely rely on three functionality-enhancing modules, i.e., semantic enhancement, attention mechanisms, and correspondence enhancement. The stacking of these modules leads to great model complexity. To unify these three modules into a simple pipeline, we introduce Relational Change Detection Transformer (RCDT), a novel and simple framework for remote sensing change detection tasks. The proposed RCDT consists…
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Wuhan University（武汉大学）；Dalian University of Technology（大连理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
Generic Knowledge Boosted Pre-training For Remote Sensing Images
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）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）
Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\…
G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；School of Automation, Hangzhou Dianzi University（自动化学院，杭州电子科技大学）；School of information engineering, Huzhou University（信息工程学院，湖州大学）；School of Data Science, Lingnan University（数据科学学院，岭南大学）
CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation
Tianmushan Laboratory, Beihang University（北京航空航天大学天目山实验室）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Astronautics, Beihang University（北京航空航天大学宇航学院）
Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation
College of Land Science and Technology, China Agricultural University（中国农业大学土地科学与技术学院）；Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs（农业农村部农业灾害遥感重点实验室）；Faculty of Geosciences and Engineering, Southwest Jiaotong University（西南交通大学地质科学与工程学院）；School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；Henan Polytechnic University（河南理工大学）；Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines, Ministry of Natural Resources of the People’s Republic of China（矿产资源时空信息与生态修复重点实验室）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Supercomputing Center in Shenzhen, Shenzhen, China（深圳国家超算中心）；Ministry of Education Key Laboratory for Earth System Modeling and the Department of Earth System Science, Tsinghua University（地球系统模拟教育部重点实验室和清华大学地球系统科学系）
STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection
School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Earth Sciences, Zhejiang University（地球科学学院）；Innovation Center of Yangtze River Delta, Zhejiang University（浙江大学长三角创新中心）
Remote sensor image object detection is an important technology for Earth observation, and is used in various tasks such as forest fire monitoring and ocean monitoring. Image object detection technology, despite the significant developments, is struggling to handle remote sensor images and small-scale objects, due to the limited pixels of small objects. Numerous existing studies have demonstrated that an effective way to promote small object detection is to introduce the spatial context. Meanwhile, recent researches for image classification have shown that spectral convolution operations can p…
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
Towards Remote Sensing Change Detection with Neural Memory
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程学院，电子科技大学）
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）
Data61, CSIRO（澳大利亚联邦科学与工业研究组织数据61中心）；CSIRO Agriculture & Food（澳大利亚联邦科学与工业研究组织农业与食品研究所）；ICRAR, The University of Western Australia（西澳大利亚大学国际射电天文学研究中心）
Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of interest, this remains a challenging task. In recent years, deep convolutional neural networks have started being used in remote sensing applications and demonstrate state of the art performance for pixel level classification of objects. \textcolor{black}{Here we propose a reliable framework for performant results for the task of semantic segmentation …
