Progressive Attention-Based Feature Recovery With Scribble Supervision for Saliency Detection in Optical Remote Sensing Image

Salient object detection (SOD) task for optical remote sensing images (RSIs) plays an important role in many remote sensing applications. Most of the existing methods train their networks depending on a large amount of pixel-wise datasets. However, such expensive and time-consuming training setting prevents the approaches becoming flexible and scalable solutions. To this end, we explore efficient SOD for optical RSIs based on easily accessible weak supervision source. In this work, we propose a novel end-to-end progressive attention-based feature recovery framework with scribble supervision. Specifically, to better locate challenging salient objects in optical RSIs, an object position module (OPM) is proposed to capture and enhance the long-range semantic dependence of objects’ position information, which depends on the complementary attention mechanism. And to restore the entire salient objects, a context refinement module (CRM) is proposed, which extract local contextual information for better propagating high-level semantics to low-level details. Moreover, to improve the adaptability of the network to the changing scenarios of optical RSIs, we propose a salient region correcting (SRC) mechanism to help the predicted salient regions rectify their saliency values by constraining the saliency relationship between predictions from different augmentation models. In addition, due to the lack of dataset for weakly supervised SOD for optical RSIs, we relabeled an existing large-scale optical RSIs dataset with scribbles, namely EORSSD-S. Experimental results on benchmark datasets demonstrate that the proposed method can outperform other weakly supervised SOD methods. And the proposed method even outperformed some fully supervised methods. https://github.com/melonless/PAFR.