Abstract

ABSTRACT To tackle the nonlinear radiation distortions (NRDs) in multimodal remote sensing images, this letter proposes a multimodal remote sensing image registration method that utilizes phase symmetry and rank-based local self-similarity (LSS). First, the local phase information of the images is utilized to construct a phase symmetry map, which is then used to detect features using the Features from Accelerated Segment Test (FAST) algorithm. Subsequently, a new feature descriptor that combines rank-based local self-similarity and phase congruency, is generated. Finally, the Fast Sample Consensus (FSC) method is employed to eliminate outliers. Experimental results on publicly available multimodal remote sensing image datasets demonstrate that the proposed method outperforms the state-of-the-art methods in terms of number of correct matches, precision, and root mean squared error (RMSE).

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