Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model's performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art.
翻译:利用遥感图像进行建筑物探测和变化探测可以帮助城市和救援规划。此外,这些模型还可以用于在自然灾害发生后进行破坏评估。目前,大多数现有的探测模型只使用一个图像(灾前图像)来探测建筑物。这基于灾后图像会降低模型的性能的理念,因为有被破坏的建筑物存在。在本文中,我们提出了一个称为Siamese的模型,称为Siameix Former,它使用灾前和灾后图像作为投入。我们的模型有两个编码器,并有一个等级变压器结构。两个编码器中的每个阶段的输出都给了一个时间变压器,用于特征聚合,其调试方法是从灾前图像中产生查询,以及(关键值,价值)是灾后图像生成的。为了这个目的,还考虑到地貌融合中的时间特征特征特征特征。另一个好处是,它们可以比CNN更好地保持变压器生成的大型可容纳的域。最后,将时间变压器的输出结果提供给每个阶段简单的MP DCD变压器,每个阶段的元变压器用于建立一个简单的MLP 和变制数据。SAISFors,用来在SD上, CD 数据库上对数据库和变换数据进行评估。