An Arbitrary-Oriented Object Detector Based on Variant Gaussian Label in Remote Sensing Images

Arbitrary-oriented remote sensing object detection is a challenging task because of the periodicity of the angle and large variety of aspect ratios. To address these issues, this letter proposes two simple yet powerful methods: a variant Gaussian label (VGL) generating method and a channel-wise pixel attention (CPA) module. Specifically, VGL is generated to handle the periodicity of the angle and produce various labels to adapt diverse aspect ratios of objects. Then, CPA is utilized to fuse features between channels and pixels, to obtain a global receptive field and extract more robust features. In addition, we collect a dataset, namely, Northwestern Polytechnical University Very-High-Resolution (NWPU VHR-10-R), which is relabeled with oriented bounding boxes based on NWPU VHR-10. To evaluate our proposed method, experiments are conducted on several publicly available benchmark datasets, including the High Resolution Ship Collections 2016 (HRSC2016) and NWPU VHR-10-R. Experimental results show that our method achieves substantial gains compared with baseline approaches.