@inproceedings{fa9f229b3f1e4ceaa8a4625a5574d3cd,
title = "Semantic segmentation of aerial images with explicit class-boundary modeling",
abstract = "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.",
keywords = "Aerial imagery, CNN, FCN, Semantic-segmentation, VHSR",
author = "D. Marmanis and K. Schindler and Wegner, {J. D.} and M. Datcu and U. Stilla",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8128165",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5165--5168",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}