Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images

The optical remote sensing images (RSIs) show various spatial resolutions and cluttered background, where salient objects with different scales, types, and orientations are presented in diverse RSI scenes. Therefore, it is inappropriate to directly extend cutting-edge saliency detection methods for conventional RGB images to optical RSIs. Besides, the existing saliency models targeting RSIs often render imperfect saliency maps, where some of them are with coarse boundary details. To solve this problem, this article attempts to introduce the edge information to precisely detect salient objects in RSIs. Accordingly, we propose an edge-aware multiscale feature integration network (EMFI-Net) for salient object detection by conducting multiscale feature integration under the explicit and implicit assistance of salient edge cues. Specifically, our network contains two parts including the encoder and decoder. First, the encoder extracts multiscale deep features from three RSIs with different resolutions, where the high-level deep semantic features from three RSIs are integrated using a cascaded feature fusion module. Second, the encoder explicitly enriches the multiscale deep features by integrating the salient edge cues extracted by a salient edge extraction module. Meanwhile, we also implicitly deploy an edge-aware constraint to the supervision of the saliency map prediction by introducing a hybrid loss function. Finally, the decoder integrates the enriched multiscale deep features in a coarse-to-fine way, yielding a high-quality saliency map. The experiments conducted on two public optical RSI datasets clearly prove the effectiveness and superiority of the proposed EMFI-Net against the state-of-the-art saliency models.