Volume III-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 9-16, 2016
https://doi.org/10.5194/isprs-annals-III-1-9-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 9-16, 2016
https://doi.org/10.5194/isprs-annals-III-1-9-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Jun 2016

01 Jun 2016

HOPC: A NOVEL SIMILARITY METRIC BASED ON GEOMETRIC STRUCTURAL PROPERTIES FOR MULTI-MODAL REMOTE SENSING IMAGE MATCHING

Yuanxin Ye1,2 and Li Shen1,2 Yuanxin Ye and Li Shen
  • 1State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, 611756, China
  • 2Collaborative innovation center for rail transport safety, Ministry of Education, Southwest Jiaotong University, 611756, China

Keywords: Multi-modal Remote Sensing Image, Image Matching, Phase Congruency, Similarity metric, HOPC

Abstract. Automatic matching of multi-modal remote sensing images (e.g., optical, LiDAR, SAR and maps) remains a challenging task in remote sensing image analysis due to significant non-linear radiometric differences between these images. This paper addresses this problem and proposes a novel similarity metric for multi-modal matching using geometric structural properties of images. We first extend the phase congruency model with illumination and contrast invariance, and then use the extended model to build a dense descriptor called the Histogram of Orientated Phase Congruency (HOPC) that captures geometric structure or shape features of images. Finally, HOPC is integrated as the similarity metric to detect tie-points between images by designing a fast template matching scheme. This novel metric aims to represent geometric structural similarities between multi-modal remote sensing datasets and is robust against significant non-linear radiometric changes. HOPC has been evaluated with a variety of multi-modal images including optical, LiDAR, SAR and map data. Experimental results show its superiority to the recent state-of-the-art similarity metrics (e.g., NCC, MI, etc.), and demonstrate its improved matching performance.