Perspective-SIFT: An efficient tool for low-altitude remote sensing image registration

• We use homographic transform to simulate multi-view perspective distortion.
• A two-resolution scheme has been used to speed up the sampling process.
• We prove that the complexity of PSIFT is about twice that of ASIFT.
• PSIFT outperforms state-of-the-art methods when images suffer severe perspective distortion.

This paper presents an automated image registration approach that is robust to perspective distortions. State-of-the-art method affine-SIFT uses affine transform to simulate various viewpoints to increase the robustness of registration. However, affine transformation does not follow the process by which real-world images are formed. To solve this problem, we propose a perspective scale invariant feature transform (PSIFT) that uses homographic transformation to simulate perspective distortion. As for ASIFT, PSIFT is based on the scale invariant feature transform (SIFT) and has a two-resolution scheme, namely a low-resolution phase and a high-resolution phase. The low-resolution phase of PSIFT simulates several image views following a perspective transformation by varying two camera axis orientation parameters. Given those simulated images, SIFT is then used to extract features and find matches among them. In the high-resolution phase, the perspective transformations which lead the largest number of matches in the low-resolution stage are selected to generate SIFT features on the original images. Experimental results obtained on three categories of low-altitude remote sensing images and Morel–Yu's dataset show that PSIFT outperforms significantly the state-of-the-art ASIFT, SIFT, Random Ferns, Harris-Affine, MSER and Hessian Affine, especially when images suffer severe perspective distortion.