Weakly supervised learning based on scribble annotations in target extraction of remote sensing images has drawn much interest due to scribbles' flexibility in denoting winding objects and low cost of manually labeling. However, scribbles are too sparse to identify object structure and detailed information, bringing great challenges in target localization and boundary description. To alleviate these problems, in this paper, we construct two inner structure-constraints, a deformation consistency loss and a trainable active contour loss, together with a scribble-constraint to supervise the optimization of the encoder-decoder network without introducing any auxiliary module or extra operation based on prior cues. Comprehensive experiments demonstrate our method's superiority over five state-of-the-art algorithms in this field. Source code is available at https://github.com/yitongli123/ISC-TE.

翻译：基于涂鸦标注的弱监督学习在遥感图像目标提取中因其标注灵活性高、人工成本低，在描述蜿蜒目标方面引起广泛关注。然而涂鸦标注过于稀疏难以辨识目标结构与细节信息，给目标定位和边界描述带来巨大挑战。为缓解这些问题，本文构建了两种内部结构约束——变形一致性损失与可训练活动轮廓损失，结合涂鸦约束共同监督编码器-解码器网络的优化过程，无需引入任何辅助模块或基于先验线索的额外操作。综合实验表明，本方法在该领域五个最新算法中具有显著优越性。源代码已开源至 https://github.com/yitongli123/ISC-TE。