Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

Abstract

The efficient semantic segmentation of buildings in high spatial resolution remote sensing images is a technical prerequisite for land resource management, high-precision mapping, construction planning and other applications. Current building extraction methods based on deep learning can obtain high-level abstract features of images. However, the extraction of some occluded buildings is inaccurate, and as the network deepens, small-volume buildings are lost and edges are blurred. Therefore, we introduce a multi-resolution attention combination network, which employs a multiscale channel and spatial attention module (MCAM) to adaptively capture key features and eliminate irrelevant information, which improves the accuracy of building extraction. In addition, we present a layered residual connectivity module (LRCM) to enhance the expression of information at different scales through multi-level feature fusion, significantly improving the understanding of context and the capturing of fine edge details. Extensive experiments were conducted on the WHU aerial image dataset and the Massachusetts building dataset. Compared with state-of-the-art semantic segmentation methods, this network achieves better building extraction results in remote sensing images, proving the effectiveness of the method.

An Efficient Semantic Segmentation Method for Remote-Sensing Imagery Using Improved Coordinate Attention

“…Zeng et al [19] proposed a new cross-scale semantic feature network by using the multiscale convolution module to obtain multiscale context from different receptive fields. Liu et al [20] developed a multi-resolution attention model based on multiscale channel and spatial attention for exacting important features. Xu et al [21] proposed a multiscale fusion network with atrous spatial pyramid pooling and varisized convolutions to effectively extract and fuse the features from multi-modal images.…”