Abstract

A large number of evenly distributed conjugate points (CPs) in entirely overlapping regions of the images are required to achieve successful co-registration between very-high-resolution (VHR) remote sensing images. The CPs are then used to construct a non-linear transformation model that locally warps a sensed image to a reference image’s coordinates. Piecewise linear (PL) transformation is largely exploited for warping VHR images because of its superior performance as compared to the other methods. The PL transformation constructs triangular regions on a sensed image from the CPs by applying the Delaunay algorithm, after which the corresponding triangular regions in a reference image are constructed using the same CPs on the image. Each corresponding region in the sensed image is then locally warped to the regions of the reference image through an affine transformation estimated from the CPs on the triangle vertices. The warping performance of the PL transformation shows reliable results, particularly in regions inside the triangles, i.e., within the convex hulls. However, the regions outside the triangles, which are warped when the extrapolated boundary planes are extended using CPs located close to the regions, incur severe geometric distortion. In this study, we propose an effective approach that focuses on the improvement of the warping performance of the PL transformation over the external area of the triangles. Accordingly, the proposed improved piecewise linear (IPL) transformation uses additional pseudo-CPs intentionally extracted from positions on the boundary of the sensed image. The corresponding pseudo-CPs on the reference image are determined by estimating the affine transformation from CPs located close to the pseudo-CPs. The latter are simultaneously used with the former to construct the triangular regions, which are enlarged accordingly. Experiments on both simulated and real datasets, constructed from Worldview-3 and Kompsat-3A satellite images, were conducted to validate the effectiveness of the proposed IPL transformation. That transformation was shown to outperform the existing linear/non-linear transformation models such as an affine, third and fourth polynomials, local weighted mean, and PL. Moreover, we demonstrated that the IPL transformation improved the warping performance over the PL transformation outside the triangular regions by increasing the correlation coefficient values from 0.259 to 0.304, 0.603 to 0.657, and 0.180 to 0.338 in the first, second, and third real datasets, respectively.

Highlights

  • The development and launch of satellites equipped with very-high-resolution (VHR) optical sensors has allowed researchers in the remote sensing field to exploit VHR multi-temporal images for a wide range of applications [1,2,3]

  • The sensed image was generated from this image but included non-linear geometric distortions to clearly check the warping performance according to the parameter values in the improved piecewise linear (IPL) transformation

  • The main purpose of the experiment implemented on the simulated dataset was to define an optimal range of the important two parameters used in the IPL transformation, i.e., the number of additional pseudo-conjugate points (CPs) (N) extracted along the image boundary with the same interval and the number of CPs (K) closest to the pseudo-CPs used for determining the location of the pseudo-CPs in the reference image

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Summary

Introduction

The development and launch of satellites equipped with very-high-resolution (VHR) optical sensors has allowed researchers in the remote sensing field to exploit VHR multi-temporal images for a wide range of applications [1,2,3]. Image co-registration, the process of geometrically overlaying images acquired over the same areas at different times, should be carried out to exploit the images for time-series remote sensing applications [6,7,8,9,10]. The sensed image is warped to the geometric location of the reference image through non-linear transformation models such as thin-plate spline, multiquadric, local weighted mean (LWM), and piecewise linear (PL) used for VHR image warping [12,13]. PL transformation is widely used because of its superior performance as compared to the other warping methods [12,14,15]

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