GSDDet: Ground Sample Distance Guided Object Detection for Remote Sensing Images

Object detection for remote sensing images (ODRSI) is an important task in computer vision. Effective algorithms inspired by oriented object detection have been proposed recently. However, a major challenge still remains. Different categories of objects may be similar under different scales, causing cross-scale confusion. Different from natural images, remote sensing images have a consistent scale within the same image, which is generally referred to as Ground Sample Distance (GSD). In this paper, we show, that GSD can be utilized to address the cross-scale confusion problem, and effectively boost the performance of ODRSI. Specifically, we propose GSDDet, which embeds the deep features that represent GSD constraints to decrease the cross-scale confusion between different object categories. In GSDDet, a deep GSD classification network is first designed to extract the GSD deep features from remote sensing images. Then, the GSD deep feature is coupled with an attention framework to detect multiple categories of objects. Due to the simplicity of our framework, GSDDet can be applied to improve both one-stage and two-stage methods. Experiments demonstrate that GSDDet outperforms state-of-the-art methods on challenging benchmarks, including DOTA-v1.0, DOTA-v1.5, and HRSC2016. The source code will be released upon publication.