Abstract
This paper presents a novel point cloud optimization method of low altitude remote sensing image based on multi-channels. The proposed method is designed to be especially effective for enhancing the accuracy of stereo matching. In order to fully exploit the information of multi-channel images, a weighted multi-channel intensity is employed instead of gray intensity. Then, an error equation is built to compute the optimal point according to the LSM method, space geometry relationship and collinear equation constraint. Compared with the traditional LSM method, the proposed method can achieve higher accuracy 3D point cloud data, since it can enrich the information conveys in the error equation and make the image matching robust. Comparison studies and experimental results prove the high accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.
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