DHC-Net: A Remote Sensing Object Detection Under Haze and Class Imbalance

Abstract

Object detection in remote sensing images is crucial in numerous fields; however, it becomes highly challenging under adverse weather circumstances. Given that previous remote sensing image object detection methods were designed based on normal weather conditions and ideal datasets, they are not beneficial for detection under real-world haze conditions and with class-imbalanced data. In this work, an adaptive dehazing centroid contrastive network (DHC-Net) is proposed to address the aforementioned issues. This network consists of an adaptive dehazing module and a centroid-guided contrastive learning approach. The adaptive dehazing module learns the image content to generate adaptive dehazing parameters, thus alleviating the influence of haze on the quality of remote sensing images. The centroid-guided contrastive learning approach is particularly designed to address the issue of imbalanced datasets. Integrating centroid vectors with actual samples in each training batch guarantees that each class is sampled at least once, effectively preventing the undersampling of minority classes. Moreover, dynamic weighted sampling based on prediction confidence guides the model to give priority to smaller classes, remarkably improving its ability to handle imbalanced data. Extensive experiments on the DOTA-v2.0, DOTA-v2.0Haze, RTTS, and HazeNet datasets demonstrate that DHC-Net is outstanding in handling haze conditions in remote sensing data, substantially enhancing target detection accuracy, even in the presence of imbalanced object classes. The source code will be available at https://github.com/Linghuaqian1/DHC_Net