Adap-EMD: Adaptive EMD for Aircraft Fine-Grained Classification in Remote Sensing

Abstract

Aircraft classifiers in remotely sensed images based on deep convolutional neural networks play a significant role in military. However, in practical applications, there is a lack of remote sensing fine-grained aircraft data. In this study, we demonstrate that few-shot learning (FSL) can be effectively used for fine-grained identification of aircraft and propose a new classifier-adaptive earth mover’s distance (Adap-EMD) for recognition of few-sample fine-grained aircraft. Adap-EMD consists of an efficient block attention mechanism (EBAM) and an adaptive feature measurement filter (AFMF). The EBAM effectively fuses channel and spatial correlation to capture global features with more pixel-wise relevance and contextual information. The non-parametric AFMF expresses the key information from the adapted emphasizing feature map to achieve a more accurate similarity measurement. Our model outperforms state-of-the-art models on a major few-shot aircraft fine-grained recognition benchmark dataset, introducing only a few additional computations.