Phase Correlation Decomposition: The Impact of Illumination Variation for Robust Subpixel Remotely Sensed Image Matching

Abstract

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence.