Abstract

Existing image matching is mostly limited to homologous images, but there are few researches on heterogeneous image matching such as UAV images and satellite remote sensing images. Aiming at the slow speed and low accuracy of traditional image matching methods when matching small-scale UAV images with satellite remote sensing images, a secondary matching method based on image edge features is proposed. The gradient information calculates the normalized cross-correlation formula to obtain the first matching result, and then obtains the optimal matching result through the shape context algorithm. Experimental results show that this method can well overcome the influence of factors such as illumination, low resolution, rotation, etc., and match UAV images and remote sensing images in a short time. The average precision and recall of the algorithm can reach 67.75% and 64.66%. Compared with the traditional SURF algorithm, it has faster matching speed and better matching accuracy.

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