Multimodal Remote Sensing Sparse Registration With a Global-Local Descriptor

Multimodal image registration is a key procedure in remote sensing applications (such as remote sensing image stitching), which faces significant challenges including radiometric discrepancies and local geometric deformations caused by the differences of both sensor and imaging parameters. Traditional methods remove coarse error using global features, making it difficult to identify misregistrations at early stage, thus limiting registration accuracy improvement. When existing convolutional registration neural networks extract deep features, shallow local feature information is usually lost because the network gradually focuses on high-level abstract features, causing local details to be simplified or lost in the global feature construction. Solving this problem will greatly increase the complexity of the model, and the network needs to reorganize and train the data according to specific tasks, which is time-consuming. To address these issues, this letter develops a hybrid registration model with a global-local descriptor. Specifically, we first obtain improved RIFT keypoints via combining rotated and scale invariant corner points produced by the integral scale detection Min-moment with extracted edge points generated by the FAST detection Max-moment. Then, a global-local descriptor is constructed by combining the improved RIFT descriptor with the LoFTR coarse-grained feature descriptor. Finally, a 0–1 distance allocation matrix is formulated to improve the registration success rate (SR). The experimental results show that the proposed method has a powerful capability in improving both generalization and accuracy and outperforms mainstream methods, even the average number of correctly registered correspondences is about two times and 1.7 times higher than LoFTR and RIFT, respectively.