Low-Level Feature Enhancement Network for Semantic Segmentation of Buildings

In recent years, convolutional neural networks (CNNs) have been widely used in extracting buildings from remote sensing images. Both semantic representation and spatial location details are crucial for this task. We propose the methods to enhance the performance of semantic segmentation by using these low-level features considering that man-made buildings in aerial images have strong textures and edges. Texture Enhancement Attention Module (TEAM) is proposed to strengthen feature in the position with rich texture and improve the semantic representation. Edge Extraction Module (EEM) is applied for directly guiding spatial details learning, which starts with super-resolution maps created by Super-Resolution Module (SRM). Detail Supplement Module (DSM) is designed to further provide the details for decoder. On this basis, we propose a low-level feature enhancement network (LFENet) for semantic segmentation of buildings. Experimental results on two aerial datasets show that our works greatly improve the accuracy over the baseline and other models.