@inproceedings{6d150cba05e54fa09a0c8aeb3e6bf89c,
title = "Hybrid Feature Embedding for Automatic Building Outline Extraction",
abstract = "Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with this problem. We also designed a triple-branch decoder structure to handle different features generated by encoder. Experiment results show that our model outperforms other baseline model on two datasets, achieving 91.1% mIoU on Vaihingen and 83.8% on Bing huts.",
keywords = "active contour, aerial image, building outline, Transformer",
author = "Weihang Ran and Wei Yuan and Xiaodan Shi and Zipei Fan and Ryosuke Shibasaki",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10282867",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3652--3655",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}