Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection. Specifically, to increase the variability of synthetic data, we randomly set the parameters during rendering, such as the size of the instance and the class of background images. In order to make the synthetic images more realistic, we then refine the synthetic images at the pixel level using CycleGAN with real unlabeled images. We also fine-tune the model with a small amount of real data, to obtain a higher accuracy. Experiments on NWPU VHR-10, UCAS-AOD and DIOR datasets demonstrate that the proposed method can be applied for augmenting insufficient real data.
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）
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）
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）
Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches
Huzhou University（湖州大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；KOKONI 3D, Moxin (Huzhou) Tech. Co., LTD.（墨芯（湖州）科技有限公司（KOKONI 3D））
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
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）
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）
VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；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 Science and Technology, Beijing Forestry University（北京林业大学信息科学与技术学院）
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
the University of Tokyo（东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Disaster Prevention Research Institute, Kyoto University（京都大学防灾研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；Hiroshima University（广岛大学）
Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in 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）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
Graph Information Bottleneck for Remote Sensing Segmentation
School of computer and Information Engineering, Central South University of Forestry and Technology（计算机与信息工程学院，中央南大学林业科技学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection
School of Artificial Intelligence and Data Science（人工智能与数据科学学院）；University of Science and Technology of China（中国科学技术大学）；Department of Information Engineering（信息工程系）；The Chinese University of Hong Kong（香港中文大学）
Exploring Models and Data for Remote Sensing Image Caption Generation
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（中国科学院西安光机精密机械研究所）；The University of Chinese Academy of Sciences（中国科学院大学）
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
