Fully Residual Convolutional Neural Networks for Aerial Image Segmentation

Abstract

Semantic segmentation from aerial imagery is one of the most essential tasks in the field of remote sensing with various potential applications ranging from map creation to intelligence service. One of the most challenging factors of these tasks is the very heterogeneous appearance of artificial objects like buildings, cars and natural entities such as trees, low vegetation in very high-resolution digital images. In this paper, we propose an efficient deep learning approach to aerial image segmentation. Our approach utilizes the architecture of fully convolutional network (FCN) based on the backbone ResNet101 with additional upsampling skip connections. Besides typical color channels, we also use DSM and normalized DSM (nDSM) as the input data of our models. We achieve overall accuracy of 91%, which is in top 4 among 140 submissions from all over the world on the well-known Vaihingen dataset from ISPRS 2D Semantic Labeling Contest. Especially, our approach yields better results then all state-of-the-art methods in segmentation of car objects.