Weakly-supervised ROI extraction method based on contrastive learning
  for remote sensing images
Lingfeng He Mengze Xu Jie Ma
ROI extraction is an active but challenging task in remote sensing because of
the complicated landform, the complex boundaries and the requirement of
annotations. Weakly supervised learning (WSL) aims at learning a mapping from
input image to pixel-wise prediction under image-wise labels, which can
dramatically decrease the labor cost. However, due to the imprecision of
labels, the accuracy and time consumption of WSL methods are relatively
unsatisfactory. In this paper, we propose a two-step ROI extraction based on
contractive learning. Firstly, we present to integrate multiscale Grad-CAM to
obtain pseudo pixelwise annotations with well boundaries. Then, to reduce the
compact of misjudgments in pseudo annotations, we construct a contrastive
learning strategy to encourage the features inside ROI as close as possible and
separate background features from foreground features. Comprehensive
experiments demonstrate the superiority of our proposal. Code is available at
https://github.com/HE-Lingfeng/ROI-Extraction