An efficient change detection method for disaster-affected buildings based on a lightweight residual block in high-resolution remote sensing images

Abstract

ABSTRACT After a natural disaster, it is critical to perform a damage assessment of affected buildings rapidly and accurately to meet emergency response requirements. With the development of deep learning (DL) technology, the change detection (CD) method based on fully convolutional networks has been used for disaster analysis and assessment. However, the main limitations of most present methods are their large number of parameters, high computational complexity, and limited deployment equipment resources, which affect the real-time performance of disaster analysis. Therefore, to make a trade-off in the relationship between the accuracy and efficiency of damaged building CD, this research proposes an efficient method based on a lightweight residual block (LRB) in bitemporal high-resolution remote sensing (RS) images (LRBNet). LRBNet is an encoder – decoder structure with the Siamese network and UNet++ as the backbone. It uses our LRB in the whole network to replace the original convolution unit, which can extract features to reduce computational complexity. The LRB consists of the lightweight compression module (LCM) and efficient channel attention (ECA) to learn the essential damage information in the feature map and improve the feature expression ability efficiently. Moreover, the multilevel damage feature aggregation attention module is the last process of the network and performs fine-grained aggregation of the multilevel disaster change features and emphasizes global semantic and spatial information to maximize the representative damage change characteristics. Furthermore, we conducted a series of experiments on the global-scale disaster xBD dataset (including 19 different disaster events). Our method not only accurately evaluated the level of disaster but also had favourable detection efficiency, achieving a good trade-off between recognition accuracy and computational resources.