基于全局结构差异与局部注意力的变化检测

Damage assessment with global differences and local attention

检测由自然灾害造成的不同变化,对于有效地指导人道主义援助和灾难响应行动来说至关重要.但是灾害发生的地区通常面积大、地面环境复杂,导致检测其变化具有较大的挑战性.现有的评估方法通常依靠人工来进行判别,不适用于多种灾害的检测.本文提出了一种新颖的变化检测模型(change transformer,CHTR),基于双时序遥感图像来同时进行建筑分割和多级变化检测两个任务.本文结合卷积神经网络擅长学习局部细节特征和Transformer可以建模长程依赖关系的优势,采用混合卷积神经网络和Transformer的架构作为编码器.考虑到自然灾害通常会对复杂环境中的建筑物造成不同程度的破坏,本文提出了一种全局差异模块,以捕获全局变化模式,提高对双时序图像之间变化的整体认识.进一步设计了一种局部门控注意力模块,以学习多级别变化之间的局部依赖性,增强对不同变化的判别能力.在目前最大的建筑物损毁评估数据集(xBD)上进行的大量实验表明,本文提出的方法在建筑分割和变化检测两个任务上都取得了更好的结果.

Detecting the different changes caused by a natural disaster is critical for effectively directing humanitarian assistance and disaster response operations. However, it is challenging due to the large-scale disaster areas and complex ground environments. Existing assessment methods are usually labor-intensive and unsuitable for multiple disasters. In this paper, we propose a change transformer(CHTR) model for simultaneous building localization and multi-level change detection from dual-temporal satellite imagery. Based on the advantages that convolutional neural networks(CNNs) are good at learning detailed local features and the transformer can model long-range dependencies, we adopt a hybrid CNN-transformer architecture as the encoder. A natural disaster usually causes varying degrees of damage to buildings in a complex environment;thus, we propose a global difference module on the original features obtained by the CNN to capture the global change pattern and improve the overall awareness of the variations between dual-temporal images. Furthermore, a local gated attention module on the patches of features after the CNN is further developed to learn the local dependencies among the multi-level changes, which augments the discrimination of different changes. Extensive experiments on the largest building damage assessment dataset, xBD, demonstrate that the proposed CHTR model establishes new state-of-the-art results.