Multimodal Remote Sensing Image Robust Matching Based on Second-Order Tensor Orientation Feature Transformation

Nonrigid deformation (NRD) and image noise in multimodal remote sensing images (MRSI) lead to abrupt changes in feature directions, resulting in sensitivity to rotational variation, sparse correct matches, and high false match rates. In order to address these challenges, this article proposes a second-order tensor orientation feature transformation (SOFT) method to improve the rotational invariance of MRSI matching and increase the number of correct matches (NCMs). The SOFT method has two main contributions: 1) a novel second-order tensor orientation descriptor is constructed by generating a tensor orientation feature map using a designed second-order tensor function, which is then combined with a gradient location and orientation histogram (GLOH)-like descriptor framework to achieve robust rotational invariance in multimodal image matching and 2) an error-removal global-local iterative optimization (EGIO) is introduced, employing a skewness of mixed pixel intensity (SMPI) function to automatically select matching seed points, followed by an iterative partition optimization strategy for refining corresponding points. Experiments on 744 groups of typical MRSIs demonstrate that the SOFT method significantly outperforms nine state-of-the-art methods, achieving an average 97% improvement in the NCMs, an average 25.51% improvement in the rate of correct matches (RCMs), and an average reduction in RMSE of 2.69 pixels. The proposed SOFT method, thus, offers robust MRSI matching with strong rotational invariance and precise identification of corresponding points, proving its effectiveness for complex remote sensing scenarios. Access to experiment-related data and codes will be provided at https://skyearth.org/research/.