Improved U-Nets with inception blocks for building detection

Abstract

With the rapid increase of the world's population, urban growth management and monitoring have become an important component in environmental, social, and economic terms. In general, automatic detection of buildings in urban areas from high-resolution satellite imagery has become an important issue. In recent years, the U-Net architecture has become one of the most popular convolutional neural networks in terms of pixel-based image segmentation. A new deep learning architecture has been developed by combining inception blocks with the convolutional layers of the original U-Net architecture to achieve remarkably high performance in building detection. First, the width of the network is increased by adding parallel filters of different sizes to the convolutional layers in the original U-Net model, and Inception U-Net architecture is developed. For the proposed architecture, parallel layers were used only in feature extraction stage to reduce the number of parameters and computation time due to a large network size. In this context, performance comparisons were made with two different datasets. The results show that a significant improvement in F-1 and kappa scores compared to the original U-Net was achieved using the proposed architecture, and model size is dramatically reduced according to Inception UNet-v1. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)