光学精密工程2023，Vol.31Issue(24)：3618-3629,12.DOI:10.37188/OPE.20233124.3618
细粒度遥感舰船开集识别
Fine-grained remote sensing ship open set recognition
摘要
Abstract
In this study,a fine-grained remote sensing ship open-set recognition model is designed to ad-dress the limitations of traditional deep convolutional neural networks in fine-grained classification of ship images.First,a STN module based on attention mechanism is introduced before the feature extraction net-work to filter background information.In addition,a multi-scale parallel convolution structure is added af-ter the STN module to enhance the feature extraction ability of the network for local regions of different scales.The extracted features are input into the base and meta-embedded branches,to increase inter-class variance and reduce intra-class variance,strengthening the model's learning of the tail class small samples concomitantly.Finally,the classification results of the two branches are fused;known and unknown classes are distinguished according to the set threshold;and known classes are subdivided.Four types of openness experiments were conducted on the FGSCR-42 datasets with balanced and unbalanced distribu-tions.The results show that the average accuracies of the four types of openness in the balanced distribu-tion dataset are 90.5%,86.3%,85.7%,and 85.1%;the corresponding average accuracies of the un-balanced distribution dataset are 90.0%,85.1%,84.3%,and 84.1%.Compared with the current mainstream ship recognition methods,the proposed method has higher recognition accuracy and better generalization ability.关键词
注意力机制/细粒度分类/开集识别/决策融合Key words
attention mechanism/fine-grained classification/open set recognition/decision fusion分类
计算机与自动化引用本文复制引用
柳长源,李婷,兰朝凤..细粒度遥感舰船开集识别[J].光学精密工程,2023,31(24):3618-3629,12.基金项目
黑龙江省自然科学基金资助项目(No.F2016022) （No.F2016022）
国家自然科学基金资助项目(No.11804068) （No.11804068）
