Weakly Supervised Optical Remote Sensing Salient Object Detection Based on Adaptive Discriminative Region Suppression

Salient object detection in optical remote sensing images aims to detect attractive objects from optical remote sensing images, providing important prior information for many remote sensing tasks, which have received more and more attention in recent years. The existing convolutional neural network-based salient object detection networks mostly rely on pixel-level labelling. Although their detection accuracy is high, annotation cost for the data is high. In addition, it is always a difficult problem that the scales of salient objects in optical remote sensing images change significantly. To address these problems, a new weakly supervised salient object detection method for optical remote sensing images is proposed. Specifically, firstly, we introduce image-level labelling as the weakly supervised information for remote sensing image salient object detection, obtaining pseudo labels to train the saliency detection network. Secondly, we propose the Local Activation Suppression module, including the Discriminative Region Suppression module and Receptive Field Block, which can effectively spread the high response region of the object to the neighbouring low response region, improving the quality of large objects pseudo labels. Finally, the Adaptive Fusion module is proposed to raise the accuracy of pseudo labels of large and small objects, which aims to reduce the noise caused by small objects. Many experiments on a public dataset show that the proposed method is better than the existing weakly supervised learning methods for salient object detection, with better detection accuracy.