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

翻译：感兴趣区域提取是遥感图像处理中一项活跃但富有挑战性的任务，其难点在于复杂的地形地貌、模糊的边界以及标注需求。弱监督学习旨在通过图像级标签学习从输入图像到像素级预测的映射，从而显著降低人工标注成本。然而，由于标签的不精确性，现有弱监督方法的准确性和效率仍不尽人意。本文提出一种基于对比学习的两步式感兴趣区域提取方法。首先，我们引入多尺度Grad-CAM以生成边界清晰的伪像素级标注。其次，为减轻伪标注中误判的影响，我们构建了一种对比学习策略，促使感兴趣区域内部特征尽可能聚合，同时将背景特征与前景特征分离。综合实验验证了本文方法的优越性。代码已开源在https://github.com/HE-Lingfeng/ROI-Extraction