AFA-NET: Adaptive Feature Aggregation Network for Aircraft Fine-Grained Detection in Cloudy Remote Sensing Images

Abstract

Aircraft is easily covered by clouds in optical remote sensing images. It is a challenge to detect the aircraft and recognize its sub-categories in this situation. However, the methods proposed by the current research are mainly applied to high-quality images, which do not perform well on cloudy images. In this paper, an adaptive feature aggregation network called AFA-Net is proposed to solve this problem. We design a mixed self-attention module that adaptively focuses on the uncovered parts of the aircraft and its neighborhood from space and channel in feature maps. Experiments were done on the Optical Image Aircraft Detection and Recognition Data Set of the 3rd Tianzhibei Challenge. Compared with the most advanced object detection algorithms, the proposed approach achieves state-of-the-art performance.