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

Abstract

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. Afterwards, 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.