Abstract
Due to the high similarity of photovoltaic images, the difficulty of registering photovoltaic images increases while the accuracy requirement for registering images is high. This article proposed a guided spatial consensus registration algorithm based on local structural similarity constraint. First, local similarity structural constraints are established based on the characteristics of the neighborhood structure among adjacent matching feature points. Second, outliers are eliminated based on the proposed similarity structural constraints to obtain a high-precision matching feature set among a strictly constrained initial matching feature set. Third, the corresponding geometric parameter of the high-precision matching feature set is used as guided information for the selection of the final inlier set from a weakly constrained initial matching feature set. Finally, the proposed method is validated by photovoltaic images with brightness and geometric differences. The experimental results demonstrate the robustness and efficiency of the proposed algorithm in the application of photovoltaic images.
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