Hough Transform Guided Deep Feature Extraction for Dense Building Detection in Remote Sensing Images

Qingpeng Li; Yunhong Wang; Qingjie Liu; Wei Wang

ICASSP 2018accepted0 citations

Hough Transform Guided Deep Feature Extraction for Dense Building Detection in Remote Sensing Images

Qingpeng Li, Yunhong Wang, Qingjie Liu, Wei Wang

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.

BibTeX

@inproceedings{icassp2018_houghtransformgu,
  title = {Hough Transform Guided Deep Feature Extraction for Dense Building Detection in Remote Sensing Images},
  author = {Qingpeng Li and Yunhong Wang and Qingjie Liu and Wei Wang},
  booktitle = {ICASSP 2018},
  year = {2018}
}