Fine-Grained Object Detection of Satellite Video in the Frequency Domain

Abstract

Satellite video objects often have small scales and the occlusion of their distinguishable regions. Existing fine-grained object detection methods estimate object locations and categories by enhancing object features and increasing feature differences between categories. However, they fail to account for the impact of limited feature information on fine-grained detection, specifically reflected in two aspects: 1) limited pixels lead to limited features. The small pixel coverage of satellite video objects results in a low upper bound on the available feature information, hindering significant improvements in fine-grained detection accuracy and 2) limited differences exacerbate limitations. Occlusion of distinguishable regions in small-scale objects exacerbates the indistinctness of features between different fine-grained categories. It prevents the network from accurately learning unique features for certain object classes, thereby degrades detector performance. To address these challenges, we propose a frequency auxiliary network (FANet), which integrates frequency domain feature learning into fine-grained object detection networks. Specifically, we propose the spectral augmented module (SAM) to extract multispectral features from various frequency components of satellite video frames to complement spatial-domain features, enabling the network to leverage hidden semantic information from the frequency domain. In addition, to better distinguish small-scale objects from the background and emphasize fine-grained category features, we design the frequency domain attention (FDA) mechanism. FDA assigns dynamic weights to spatial and frequency domain features, suppressing background information and enhancing feature differences between fine-grained categories. Extensive experiments on the SAT-MTB dataset demonstrate that FANet achieves superior performance compared to existing methods.