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Height correction based on multiple subimage correlation

In this paper, a height correction approach is proposed based on multiple sub-image correlation in remote sensing and navigation systems. First, multiple subareas are selected in the reference image, combination of two of which consists of the two for height correction. Then the measured distance and real distance between two areas can be computed by correlation matching and perspective transformation model. With the distances, height deviation can be estimated for further processing. Considering the accuracy-loss caused by image blurring, noise, changes of scales and so on, the approach utilizes clustering method to improve accuracy. Experiments show that the proposed method can estimate the height deviation automatically and the accuracy of the proposed method is equivalent to that based on manually labeled.