MS-POFT: multiscale phase-orientation guided feature transform for multi-modal image matching

Multi-modal remote sensing image (MRSI) matching has always been a challenging task. Traditional image matching methods often fail to obtain satisfactory results in most cases due to temporal differences, complex geometric distortions, and non-linear radiometric differences (NRDs). The key to addressing MRSI matching lies in mitigating NRDs to achieve robust extraction and description of features. This paper proposes a multiscale phase-orientation guided feature transform (MS-POFT) for multi-modal image matching. Two novel strategies are investigated and integrated into MS-POFT to improve the matching performance. A phase-structured adaptive detection is designed by the complementation of phase stretching transform and adaptive sliding windows, which ensures stable feature point extraction across different scales. Then, a new feature descriptor suitable for multi-modal images, called MS-PGLOH, is constructed based on phase and gradient principal direction in multiscale space. We performed comparison experiments on various multimodal datasets from remote sensing, natural sceneries, night surveillance, medical and temporal changes. Our experimental results both in qualitative and quantitative ways show that our proposed MS-POFT outperforms other comparison methods. MS-POFT successfully matched all given image pairs, achieving satisfactory results in terms of the number of correct matches (NCM), proportion of corrections ratio (PCR), and a reduced root-mean-square error (RMSE) of approximately 1.36.