AMIO-Net: An Attention-Based Multiscale Input–Output Network for Building Change Detection in High-Resolution Remote Sensing Images

Abstract

Building change detection (CD) from remote sensing images (RSI) has great significance in exploring the utilization of land resources and determining the building damage after a disaster. This paper proposed an attention-based multi-scale input-output network, named AMIO-Net, for building CD in high-resolution RSI. It is able to overcome partial drawbacks of existing CD methods, such as insufficient utilization of information (details of building edges) of original images and poor detection effect of small targets (small-scale buildings or small-area changed buildings that are disturbed by other buildings). In AMIO-Net, the input image is scaled down to different sizes, and performed the convolution to extract features. Then the feature maps are fed into the encoding stage so that the network can fully utilize the feature information (FI) of the original image. More importantly, we design two attention mechanism modules: the pyramid pooling attention module (PPAM) and the Siamese attention mechanism module (SAMM). PPAM combines a pyramid pooling module and an attention mechanism to fully consider the global information and focus on the FI of changed pixels in the image. The input of SAMM is the parallel multi-scale output diagram of the decoding portion and deep feature maps of the network so that AMIO-Net can utilize the global contextual semantic FI and strengthen detection ability for small targets. Experiments on three datasets show that the proposed method achieves higher detection accuracy and F1 score compared with the state-of-the-art methods.