The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 439–442, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-439-2012
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 439–442, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-439-2012

  31 Jul 2012

31 Jul 2012

CO-REGISTRATION BETWEEN MULTISOURCE REMOTE-SENSING IMAGES

J. Wu1, C. Chang2, H.-Y. Tsai2, and M.-C. Liu2 J. Wu et al.
  • 1CSRSR, National Central University, Jhongli, Taiwan
  • 2Dept. of Civil Engineering, National Central University, Jhongli, Taiwan

Keywords: Registration, Least-Squares Matching, SIFT, TPS, RANSAC

Abstract. Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.