Hybrid Feature Embedding For Automatic Building Outline Extraction
Weihang Ran Wei Yuan Xiaodan Shi Zipei Fan Ryosuke Shibasaki
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.