IMPROVING CNN-BASED BUILDING SEMANTIC SEGMENTATION USING OBJECT BOUNDARIES

Abstract

Semantic segmentation is an active area of research with a wide range of applications including autonomous driving, digital mapping, urban monitoring, land use analysis and disaster management. For the past few years approaches based on Convolutional Neural Networks, especially end-to-end approaches based on architectures like the Fully Convolutional Networks (FCN) and UNet, have made great progress and are considered the current state-of-the-art. Nevertheless, there is still room for improvement as CNN-based supervised-learning models require a very large amount of labelled data in order to generalize effectively to new data and the segmentation results often lack detail, mostly in areas near the boundaries between objects. In this work we leverage the semantic information provided by the objects’ boundaries to improve the quality and detail of an encoder-decoder model’s semantic segmentation output. We use a UNet-based model with ResNet as an encoder for our backbone architecture in which we incorporate a decoupling module that separates the boundaries from the main body of the objects and thus learns explicit representations for both body and edges of each object. We evaluate our proposed approach on the Inria Aerial Image Labelling dataset and compare the results to a more traditional Unet-based architecture. We show that the proposed approach marginally outperforms the baseline on the mean precision, F1-score and IoU metrics by 1.1 to 1.6%. Finally, we examine certain cases of misclassification in the ground truth data and discuss how the trained models perform in such cases.