A radiometric and rotational invariant feature descriptor for multimodal remote sensing image matching using the monogenic signal

ABSTRACT

Multimodal remote sensing image matching is still a challenging task, and its matching performance will highly influence the subsequent applications, such as image fusion, image mosaic and change detection. To tackle this problem, we propose a radiometric and rotational invariant descriptor (RRID) using the monogenic signal for multimodal image matching. First of all, a multiscale feature asymmetry measurement is proposed to obtain more reliable detected points, in which the multiscale feature asymmetry can be computed from the intermediate results from the monogenic signal; then a multiscale local phase orientation computation algorithm is designed for the main orientation assignment of each keypoint, in which the local phase orientation can also be provided by the monogenic signal construction; finally, a circular local window around each keypoint is extracted, a histogram of local phase orientations weighted by phase congruency is calculated for the log-polar feature description. RRID is compared with other state-of-the-art multimodal image matching methods on three publicly available datasets. Experimental results have demonstrated that our proposed method outperforms the compared methods in terms of precision, recall, and F1-measure criteria and also has achieved the lowest RMSEs.

KEYWORDS:

• multimodal images
• non-linear radiometric distortions
• feature matching
• monogenic signal
• multiscale feature asymmetry

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments to this paper. Numerical calculation is supported by High-Performance Computing Center of Wuhan University of Science and Technology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used in this paper are all publicly available.