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Robust Point Correspondence with Gabor Scale-Invariant Feature Transform for Optical Satellite Image Registration

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Abstract

In this paper, a robust point correspondence algorithm is proposed to address the problems with SIFT-like methods for the optical satellite image registration. SIFT-like methods involve two issues that are rarely noticed: non-orientation selectivity in feature detection and feature redundancy in feature description. The novelty of the proposed approach is that the advantages of biologically motivated methods are adopted to resolve above two problems. Firstly, by using a 2D Gabor filter bank to model the visual cognitive computational model, intuitive and robust keypoints are detected. The proposed detector can capture salient visual properties such as the orientation and spatial frequency selectivity. Secondly, multi-characteristic scales of the keypoints are selected based on the Gabor kernel function, and then multi-feature descriptors with high discriminating power are defined. By using the proposed detector and descriptor, the feature redundancy can translate into benefits. Finally, a feature matching strategy for multi-feature descriptors is designed, to improve the reliability of feature matching. Evaluation criteria of 1-precision, RMSE and visual inspection of the matched pairs are used to demonstrate the superior performance of the proposed algorithm on optical satellite image registration.

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Acknowledgements

Funding was provided by Hunan Provincial Innovation Foundation for Postgraduate (Grant No. CX2014B021), Fund of Innovation of NUDT Graduate School (Grant No. B140406), and Hunan Provincial Natural Science Foundation of China (Grant No. 2015JJ3018).

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Authors and Affiliations

  1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Yi Hou & Shilin Zhou

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  1. Yi Hou
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  2. Shilin Zhou
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Correspondence to Yi Hou.

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Hou, Y., Zhou, S. Robust Point Correspondence with Gabor Scale-Invariant Feature Transform for Optical Satellite Image Registration. J Indian Soc Remote Sens 46, 395–406 (2018). https://doi.org/10.1007/s12524-017-0707-5

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