Abstract
Kompsat-3/3A provides along-track and across-track stereo data for accurate three-dimensional (3D) topographic mapping. Stereo data preprocessing involves conjugate point extraction and acquisition of ground control points (GCPs), rational polynomial coefficient (RPC) bias compensation, and epipolar image resampling. Applications where absolute positional accuracy is not a top priority do not require GCPs, but require precise conjugate points from stereo images for subsequent RPC bias compensation, i.e., relative orientation. Conjugate points are extracted between the original stereo data using image-matching methods by a proper outlier removal process. Inaccurate matching results and potential outliers produce geometric inconsistency in the stereo data. Hence, the reliability of conjugate point extraction must be improved. For this purpose, we proposed to apply the coarse epipolar resampling using raw RPCs before the conjugate point matching. We expect epipolar images with even inaccurate RPCs to show better stereo similarity than the original images, providing better conjugate point extraction. To this end, we carried out the quantitative analysis of the conjugate point extraction performance by comparing the proposed approach using the coarsely epipolar resampled images to the traditional approach using the original stereo images. We tested along-track Kompsat-3 stereo and across-track Kompsat-3A stereo data with four well-known image-matching methods: phase correlation (PC), mutual information (MI), speeded up robust features (SURF), and Harris detector combined with fast retina keypoint (FREAK) descriptor (i.e., Harris). These matching methods were applied to the original stereo images and coarsely resampled epipolar images, and the conjugate point extraction performance was investigated. Experimental results showed that the coarse epipolar image approach was very helpful for accurate conjugate point extraction, realizing highly accurate RPC refinement and sub-pixel y-parallax through fine epipolar image resampling, which was not achievable through the traditional approach. MI and PC provided the most stable results for both along-track and across-track test data with larger patch sizes of more than 400 pixels.
Highlights
Kompsat-3 and Kompsat-3A are Korean Earth observation/infrared satellites
The requirements for good epipolar image resampling are near zero y-parallax and x-parallax linearly proportional to the ground height; accurate sensor modeling such as that based on rational polynomial coefficients (RPCs) is necessary
The quasi-ground control points (GCPs) were back projected to the image space to perform RPC bias compensation of the stereo data
Summary
Kompsat-3 and Kompsat-3A are Korean Earth observation/infrared satellites They can acquire along-track and across-track stereo data for three-dimensional (3D) topographic mapping applications such as dense digital surface model (DSM) generation, 3D display, and topographic map drawing [1]. Stereo satellite data have high potential for many geospatial applications such as disaster monitoring [2,3], ground displacement measurement [4,5], and urban building modeling [6,7,8]. The requirements for good epipolar image resampling are near zero y-parallax and x-parallax linearly proportional to the ground height; accurate sensor modeling such as that based on rational polynomial coefficients (RPCs) is necessary. Epipolar image-resampling algorithms [11,12,13] use sensor modeling information to establish accurate correspondence between the ground object space and the image space. Accurate sensor modeling achieves less than one-pixel-level of accuracy [14,15]
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