用于光谱解混和泛锐化的空间和光谱分辨率差异很大的高光谱和彩色图像的非刚性配准

Yuan Zhou, Anand Rangarajan, P. Gader
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引用次数: 8

摘要

本文提出了一种利用点扩散函数(PSF)进行超大尺度差图像配准的框架,并将其应用于高光谱、高分辨率彩色图像的配准。该算法利用光谱响应函数(SRF)、对高光谱图像进行非刚性自由变形和对彩色图像进行刚性变换来实现最小二乘目标函数的最小化。通过交替更新两个变换和两个物理函数来解决优化问题。我们在模拟的Pavia University数据集和真实的Salton Sea数据集上执行了该框架,并将所提出的算法与其刚性变化和两种相互信息的算法进行了比较。结果表明,LSQ自由格式版本在非刚性模拟和真实数据集上具有最佳性能,在高光谱域给出1像素非刚性畸变时误差小于0.15像素。
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Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening
In this paper, we propose a framework to register images with very large scale differences by utilizing the point spread function (PSF), and apply it to register hyperspectral and hi-resolution color images. The algorithm minimizes a least-squares (LSQ) objective function with an incorporated spectral response function (SRF), a nonrigid freeform deformation applied on the hyperspectral image and a rigid transformation on the color image. The optimization problem is solved by updating the two transformations and the two physical functions in an alternating fashion. We executed the framework on a simulated Pavia University dataset and a real Salton Sea dataset, by comparing the proposed algorithm with its rigid variation, and two mutual information-based algorithms. The results indicate that the LSQ freeform version has the best performance for the nonrigid simulation and real datasets, with less than 0.15 pixel error given 1 pixel nonrigid distortion in the hyperspectral domain.
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