Semantic segmentation of aerial images with explicit class-boundary modeling

In this work we propose an end-to-end trainable supervised Deep Convolutional Neural Network (DCNN) targeting the task of semantic-segmentation with the addition of class-aware boundary detection. Through this explicit modeling of the class-boundaries, we enforce the network to extract coherent and complete objects, suppressing the uncertainty influencing these regions. Importantly, we show that class-boundary networks in conjunction with DCNN performs optimally, achieving over 90% overall accuracy (OA) on the challenging ISPRS Vaihingen Semantic Segmentation benchmark.