Remote Sensing Image
Remote sensing image analysis focuses on extracting meaningful information from images captured by satellites and aerial platforms, primarily for Earth observation applications. Current research emphasizes improving the accuracy and efficiency of various tasks, including semantic segmentation, object detection (especially oriented objects), and change detection, often leveraging deep learning models like transformers and UNets, along with innovative techniques such as prompt learning and multimodal fusion. These advancements are crucial for a wide range of applications, from precision agriculture and urban planning to environmental monitoring and disaster response, enabling more accurate and timely insights from remotely sensed data.
Papers
Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing Images
Maximilian Bernhard, Tanveer Hannan, Niklas Strauß, Matthias Schubert
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images
Wenbin Guan, Zijiu Yang, Xiaohong Wu, Liqiong Chen, Feng Huang, Xiaohai He, Honggang Chen
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation
Jieyi Tan, Yansheng Li, Sergey A. Bartalev, Shinkarenko Stanislav, Bo Dang, Yongjun Zhang, Liangqi Yuan, Wei Chen
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
Chengxi Han, Chen Wu, Haonan Guo, Meiqi Hu, Jiepan Li, Hongruixuan Chen