High-Resolution Polar Network for Object Detection in Remote Sensing Images

Abstract

Object detection is regarded as a significant research branch of optical remote sensing image analysis. Aimed at the characteristics of remote sensing image targets such as direction diversity, small scales, and dense arrangements, the state-of-the-art detectors used in optical remote sensing images are primarily extended from some general natural detection approaches based on deep convolutional neural networks (DCNNs). Nevertheless, the detectors intended for remote sensing targets are commonly based on an anchor mechanism and perform regression tasks in the Cartesian coordinate system, which requires complex multiangle anchors, inclined nonmaximum suppression (NMS), and so on. In this letter, a high-resolution polar network (HRPNet) is proposed to detect targets in remote sensing images. As an anchor-free and high-resolution detection network, HRPNet converts the detection of the oriented bounding box into the regression of one polar angle and four polar radii at the corresponding pole points in polar coordinates, which dramatically decreases the computational complexities. As revealed by extensive experiments, the proposed HRPNet shows a competitive advantage on the DOTA and HRRSD data sets.