Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images

Abstract

Existing CNN-based salient object detection (SOD) models rely heavily on large-scale pixel-level annotations, which are labor-intensive, time-consuming, and expensive. In contrast, sparse annotations (e.g., image-level or scribble) are gradually entering the SOD community. However, few efforts have been devoted to studying SOD from sparse annotations, especially in remote sensing. Moreover, sparse annotations usually contain a small amount of information, making it challenging to train a well-performing model, thereby causing its performance to lag largely behind fully-supervised models. Although some SOD methods adopt prior cues (e.g., edges) to improve performance, they usually lack targeted discrimination of object boundaries and thus provide saliency maps with poor boundary localization. To this end, in this paper, we propose a novel weakly-supervised SOD framework to predict the saliency of remote sensing images from sparse scribble annotations. To achieve it, we first construct the scribble-based remote sensing saliency dataset by relabeling an existing large-scale SOD dataset with scribbles, namely S-EOR dataset. After that, we present a novel scribble-based boundary-aware network (SBA-Net) for remote sensing saliency detection. Specifically, we design a boundary-aware module (BAM) to explore object boundary semantics, which is explicitly supervised by high-confidence object boundary (pseudo) labels generated by the boundary label generation (BLG) module, forcing the model to learn features that highlight object structures and thus boosting object boundary localization. Extensive quantitative and qualitative comparisons of two public remote sensing SOD datasets show that the proposed method outperforms current weakly supervised and unsupervised SOD methods and is highly competitive with existing fully supervised methods. Numerous ablation experiments demonstrate the effectiveness and generalization of the proposed model. The dataset and code will be publicly available at: https://github.com/ZhouHuang23/SBA-Net.