Salient Object Detection Based on Progressively Supervised Learning for Remote Sensing Images

Abstract

Salient object detection (SOD) is a crucial task in the field of remote sensing image (RSI) processing. Weakly supervised SOD methods, which generate saliency maps by classification convolutional neural networks (CNNs), considerably reduce labor costs. However, due to the complexity of remote sensing scenes, concerns remain about weakly supervised SOD for RSIs: 1) since the pooling operations are applied in the classification CNNs, the boundary maintenance of weakly supervised methods is unsatisfactory and 2) several sophisticated postprocessing procedures are used in previous weakly supervised methods, which are inevitably time-consuming. To solve these problems, we combine the benefits of weakly and fully supervised learning and propose a new SOD method named progressively supervised learning (PSL) for RSIs. The proposed method realizes end-to-end SOD with a lightweight model under imagewise annotations. First, to reduce the demands on large-scale pixelwise annotations, we propose a pseudo-label generation method based on a classification network and gradient-weighted class activation mapping (Grad-CAM) to compute pseudo saliency maps (PSMs) for training samples and auxiliary images in a weakly supervised manner. Then, to improve the computational efficiency, we construct a feedback saliency analysis network (FSAN), where the generated PSMs are regarded as pixelwise labels. Finally, inspired by curriculum learning, we design a new denoising loss function to further reduce the effect brought by missing judgment in PSMs and enhance the detection accuracy. Comprehensive evaluations with two remote sensing data sets and a comparison with 11 methods validate the superiority of the proposed PSL model.