@inproceedings{467709740cf646e8ab761f4814bbc370,
title = "Hough Transform Guided Deep Feature Extraction for Dense Building Detection in Remote Sensing Images",
abstract = "Detecting dense buildings without elevation information is an important and challenging task in remote sensing applications. In this paper, we present a novel cascaded deep neural network architecture, incorporating multi-stage region proposal detection and Hough transform to obtain better mid-level semantic information for man-made objects. This proposed network can be trained end-to-end by multi-loss jointly. We train and test it on a large building dataset collected from Google Earth, including buildings from urban, suburban and rural areas. Experiments demonstrate great robustness and superiority of our method to various buildings over other convolutional neural network (CNN) based detection methods.",
keywords = "Building detection, CNN, Deep learning, Hough transform, Remote sensing",
author = "Qingpeng Li and Yunhong Wang and Qingjie Liu and Wei Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8461407",
language = "英语",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1872--1876",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
address = "美国",
}