文献类型：会议论文

英文题名：Research on the Extremely Small Target Identification in Aerial Remote Sensing Images with Negative Example Enhancement

作者：Li, Fan[1,2,3] Xu, Pingping[1]

第一作者：李凡;Li, Fan

机构：[1] National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, 211189, China; [2] Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing, 100091, China; [3] Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing, 100091, China

会议论文集：International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

会议日期：January 19, 2024 - January 21, 2024

会议地点：Xiamen, China

语种：英文

外文关键词：Antennas - Deep learning - Image enhancement

年份：2024

摘要：In response to the significant difficulty in detecting extremely small targets in aerialremote sensing images, this research proposes a data enhancement method that optimizes the negative example selection strategy, that is, to achieve the objective by optimizing the strategy of selecting negative samples for training during the training process on the basis of existing deep learning-based target detection methods. Taking the Faster-RCNN model as an example, we use the outcome of model error identification as the negative example in the next epoch of the training process and adjusts the loss function according to the negative example category. Experimental shows that the algorithm can effectively enhance the detection accuracy of extremely small targets in remote sensing images, with a strong screening ability for interference regions in complicated backgrounds. ? 2024 SPIE.