Aircraft Detection in Remote Sensing Images via CNN Multi-scale Feature Representation

Abstract

Aircraft detection in remote sensing images is an intractable challenge. The current aircraft detection methods have limited representative capabilities and heavy computational costs. This paper studies how to apply multi-scale feature representation of Convolutional Neural Networks (CNN) to aircraft detection by qualitatively and quantitatively analyzing the performance of Single Shot Detection (SSD) approach. At first, we find that low-level detectors are not robust enough to detect as the semantic gap issue. Therefore we propose a data driven hyper-parameter selection method to alleviate this problem by determining appropriate hyper-parameters of sliding window and default box shape. Besides, we employ a multi-scale training strategy to enhance low-level predictive detectors. Finally, we propose an accurate and efficient aircraft detection framework. Experimental results illustrate that our method could achieve 96.84% AP at 20 FPS on NVIDIA TITAN X. Compared with original SSD method, our proposed approach achived 2.13% AP improvement