A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.
@article{arxiv.1908.11822,
title = {Multi-Temporal Aerial Image Registration Using Semantic Features},
author = {Ananya Gupta and Yao Peng and Simon Watson and Hujun Yin},
journal= {arXiv preprint arXiv:1908.11822},
year = {2019}
}
Comments
Accepted to 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)