论文标题
遥感图像更改检测的语义脱钩表示学习
Semantic decoupled representation learning for remote sensing image change detection
论文作者
论文摘要
基于当代转移学习的方法来减轻变化检测中数据不足（CD）的方法主要基于Imagenet预训练。最近已将自我监督的学习（SSL）引入遥感（RS），以学习内域表示。在这里，我们为RS图像CD提出了语义脱钩的表示学习。通常，与庞大的背景相比，感兴趣的对象（例如，建筑物）相对较小。与可能由无关的土地覆盖主导的一个表示矢量的现有方法不同，我们通过利用语义掩码来解散不同语义区域的表示。我们还迫使模型区分不同的语义表示，这使下游CD任务中感兴趣的对象的识别受益。我们以毫不费力的方式构建带有语义掩码的咬合图像的数据集进行预训练。两个CD数据集的实验显示，我们的模型优于Imagenet预训练，内域监督预训练以及最近的几种SSL方法。
Contemporary transfer learning-based methods to alleviate the data insufficiency in change detection (CD) are mainly based on ImageNet pre-training. Self-supervised learning (SSL) has recently been introduced to remote sensing (RS) for learning in-domain representations. Here, we propose a semantic decoupled representation learning for RS image CD. Typically, the object of interest (e.g., building) is relatively small compared to the vast background. Different from existing methods expressing an image into one representation vector that may be dominated by irrelevant land-covers, we disentangle representations of different semantic regions by leveraging the semantic mask. We additionally force the model to distinguish different semantic representations, which benefits the recognition of objects of interest in the downstream CD task. We construct a dataset of bitemporal images with semantic masks in an effortless manner for pre-training. Experiments on two CD datasets show our model outperforms ImageNet pre-training, in-domain supervised pre-training, and several recent SSL methods.
