A multi-level image alignment method for aerial image and road-based geo-parcel data

Image alignment between aerial image and geo-parcel data is a meaningful and challenging task in remote sensing field. In this article, a deep learning framework based on multi-level progressive architecture focusing on aerial image and road based geo-parcel data alignment is proposed. Firstly, an image segmentation with U-Net model as a preprocessing work is applied to obtain the road binary image of aerial image, which benefits following image alignment by turning multi-modal image alignment into mono-modal image alignment. After-wards, multi-scale deep features are extracted to take part in the proposed image alignment network. The proposed image alignment network consists of a global multiple homographies prediction module and a local flow map estimation module, forming a coarse-to-fine and global-to-local multi-level paradigm. Finally, synthetic image datasets are generated to test and verify the performance of proposed method. The experiments on the synthetic aerial image and road based geo-parcel data including Mnih dataset and Deep Globe road dataset demonstrate that the proposed method can align image pairs effectively, and the proposed method achieves a certain increase in matching performance by comparing with recent existing alignment methods qualitatively and quantitatively.

Automated summary

In this article, a deep learning framework is proposed for the alignment between aerial image and road based geo-parcel data. The framework includes a pre-processing step of image segmentation, followed by multi-scale deep feature extraction, and a multi-level alignment network. Synthetic image datasets are used to test and verify the performance of the proposed method, which shows effective alignment of image pairs and improvement in matching performance compared to existing methods.