IRSNET: An Inception-Resnet Feature Reconstruction Model for Building Segmentation

Abstract

Effective analysis of remote sensing images remains a challenging topic in deep learning research field. This paper proposes a semantic segmentation approach based on the inception architecture. Specifically, the combination of residual network and Inception improves the feature extraction capability of encoder network. In addition, this paper discusses the problem of unbalanced information allocation caused by concatenated operation in the up-sampling process of network models such as Unet, and the RCSE module is proposed to complete feature reconstruction to solve this problem. The approach ensures accurate assignment of semantic labels to the buildings in the aerial images. Experiments based on the dataset proposed by CrowdAi certify the effectiveness of our approach with a 3.7% IoU improvement compared to Unet.