Abstract
Remote sensing image registration (RSIR) has been performed in various RS applications for decades. However, how to adaptively register multi-type (multi-view, multi-temporal, and multi-sensor) RS images remains a challenging problem due to different degrees of local nonrigid distortions, rotation angles, and nonlinear intensity differences between such images. This paper presents a general RSIR method for multi-type RS images. The multi-type mixed feature descriptor (MMFD) is first constructed by combining the respective advantages of channel features of orientated gradients (CFOG) descriptor, speeded up robust features (SURF) local distance, and neighbouring structure descriptor. According to prematching of SURF local distance and CFOG descriptor, a dynamic threshold adjusts the feature points number adaptively. Finally, the dynamic feature selection strategy is implemented to adjust weight parameters of MMFD by the thresholding techniques. Extensive experiments on proposed method are performed over satellite and UAV datasets, and results show that the proposed method provides favorable performance (RSME of 1.59761 and 1.0811) with respect to eight state-of-the-art methods.
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Song, F., Chen, Q., Lei, T., Peng, Z. (2022). Adaptive Registration for Multi-type Remote Sensing Images via Dynamic Feature Selection. In: Wang, Y., Ma, H., Peng, Y., Liu, Y., He, R. (eds) Image and Graphics Technologies and Applications. IGTA 2022. Communications in Computer and Information Science, vol 1611. Springer, Singapore. https://doi.org/10.1007/978-981-19-5096-4_10
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DOI: https://doi.org/10.1007/978-981-19-5096-4_10
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