Aerial Image Matching based on NSST and Quaternion Exponential Moment

doi:10.23940/ijpe.18.11.p12.26632673

Abstract

Abstract: In this paper, we propose an aerial image matching algorithm based on NSST and quaternion exponential moment. Firstly, we use non-subsampled shearlet transform (NSST) to decompose the reference image and the to-be-matched image, and the scale invariant feature with error resilience (SIFER) operator is used to extract stable feature points from NSST low-frequency sub-bands and construct local feature areas respectively. Subsequently, local features of each feature area are solved by quaternion exponential moment to constitute feature vectors of such feature points for pre-matching. In the end, mismatching point pairs are removed by the random sample consensus (RANSAC) algorithm. Finally, experimental results show that compared with the SIFT and SURF algorithms, the algorithm proposed in this paper makes faster operations, has higher matching precision, and is significantly better than the other two methods in resisting rotation, noise, brightness change, and integrated disturbance.

Key words: image matching, non-subsampled shearlet transform, quaternion exponential moment, scale invariant feature with error resilience

Huan Wang, Zhenhua Jia, and Yunfeng Zhang. Aerial Image Matching based on NSST and Quaternion Exponential Moment [J]. Int J Performability Eng, 2018, 14(11): 2663-2673.

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