ORSI Salient Object Detection via Bidimensional Attention and Full-Stage Semantic Guidance

The application of optical remote sensing images (ORSIs) is prevalent in many fields. Accordingly, ORSI-oriented salient object detection (SOD) has attracted more attention in recent years. However, yet many previously proposed methods present appealing performance in natural scene images (NSIs), they are difficult to be directly extended to remote sensing images due to the more complex scenes, such as blended backgrounds and diversiform topological shapes. Most specifically designed models often fail to achieve satisfactory results due to the weak usage of edge information and the ignorance of attention loss. Besides, computational inefficiency often causes poor applicability. To solve these problems, we propose a new model, namely, Bidimensional Attention and Full-stage Semantic Guidance Network (BAFS-Net), containing an edge guidance branch and a mainstream detection branch. Concretely, edge guidance generates boundary information, in which supervision with border labels is imposed to highlight the salient regions and plays a complementary role on the main branch. The mainstream detection branch involves two important components, i.e., bidimensional attention modules (BAMs) and semantic-guided fusion modules (SGFMs). Between these two, BAM uniformly assembles channel and spatial attention in an efficient and rational manner, addressing the open issue of dimensionwisely attention computation. SGFM hammers at the fusion of high-level features and low-level features. Moreover, the semantic maps are employed to interact with SGFM in full stages. Our approach surpasses most state-of-the-art RSI-SOD methods proposed in recent years, with respect to the accuracy, parameter size, computational cost, and floating point operations per second (FLOPS). The code is available at
https://github.com/ZhengJianwei2/BAFS-Net
.