Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect and annotate bitemporal samples containing desired changes. Transfer learning from pretrained models is effective to alleviate label insufficiency in remote sensing (RS) change detection (CD). We explore the use of semantic information during pretraining. Different from traditional supervised pretraining that learns the mapping from image to label, we incorporate semantic supervision into the self-supervised learning (SSL) framework. Typically, multiple objects of interest (e.g., buildings) are distributed in various locations in an uncurated RS image. Instead of manipulating image-level representations via global pooling, we introduce point-level supervision on per-pixel embeddings to learn spatially sensitive features, thus benefiting downstream dense CD. To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask. We learn an embedding space where background and foreground points are pushed apart, and spatially aligned points across views are pulled together. Our intuition is the resulting semantically discriminative representations invariant to irrelevant changes (illumination and unconcerned land covers) may help change recognition. We collect large-scale image-mask pairs freely available in the RS community for pretraining. Extensive experiments on three CD datasets verify the effectiveness of our method. Ours significantly outperforms ImageNet pretraining, in-domain supervision, and several SSL methods. Empirical results indicate our pretraining improves the generalization and data efficiency of the CD model. Notably, we achieve competitive results using 20% training data than baseline (random initialization) using 100% data. Our code is available at https://github.com/justchenhao/SaDL_CD .

中文翻译：

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用于遥感图像变化检测的语义感知密集表示学习

有监督的深度学习模型依赖于大量的标记数据。不幸的是，收集和注释包含所需变化的双时态样本既费时又费力。预训练模型的迁移学习可有效缓解遥感 (RS) 变化检测 (CD) 中的标签不足。我们探索了预训练期间语义信息的使用。与学习从图像到标签的映射的传统监督预训练不同，我们将语义监督纳入自监督学习 (SSL) 框架。通常，多个感兴趣的对象（例如，建筑物）分布在未经处理的 RS 图像中的不同位置。而不是通过全局池化操作图像级表示，我们在每像素嵌入上引入点级监督来学习空间敏感特征，从而有利于下游密集 CD。为了实现这一点，我们通过使用语义掩码对视图之间的重叠区域进行类平衡采样来获得多个点。我们学习了一个嵌入空间，其中背景和前景点被推开，并且跨视图的空间对齐点被拉到一起。我们的直觉是由此产生的语义判别表示对不相关的变化（照明和不关心的土地覆盖）不变可能有助于改变识别。我们收集 RS 社区中免费提供的大规模图像掩码对用于预训练。在三个 CD 数据集上进行的大量实验验证了我们方法的有效性。我们的显着优于 ImageNet 预训练、域内监督、和几种 SSL 方法。经验结果表明，我们的预训练提高了 CD 模型的泛化能力和数据效率。值得注意的是，与使用 100% 数据的基线（随机初始化）相比，我们使用 20% 的训练数据获得了具有竞争力的结果。我们的代码可在https://github.com/justchenhao/SaDL_CD .