Gene

Algorithm

Statistics

Image matching

Robustness (evolution)

Biochemistry

Computer science

Geology

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Artificial intelligence

Remote sensing

Matching (statistics)

Chemistry

Delaunay triangulation

Convolutional neural network

Computer vision

IEEE Transactions on Geoscience and Remote Sensing

Multimodal remote sensing image (MRSI) high-precision matching faces significant challenges due to the large nonlinear distortions in radiation and geometry. To address this problem, we propose a novel MRSI matching method, multimodal remote sensing image matching with Delaunay triangulation constraint network (M2DT-Net), which takes advantage of deep convolutional neural networks (CNNs) and the Delaunay triangulation (DT) strategy. First, a fine-tuned CNN model is applied to learn invariant features with high robustness across MRSIs. Second, an adaptive feature descriptor distance constraint combined with the DEGENSAC refinement is employed to filter the obtained matches, and only the matches with high credibility are retained. Finally, a DT strategy is adopted to expand more underlying correct matches that are dropped inliers in the previous stage. The experimental results on seven types of MRSI pairs indicate that M2DT-Net is superior to seven state-of-the-art image matching algorithms in terms of model performance and efficiency, achieving a balance of robustness and time consumption. The image registration results further demonstrate the effectiveness of the proposed algorithm. Therefore, the proposed M2DT-Net method provides a reference for resolving common multimodal image matching problems.

Multimodal Remote Sensing Image Matching Combining Learning Features and Delaunay Triangulation