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首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >Nonrigid registration of hyperspectral and color images with vastly different spatial and spectral resolutions for spectral unmixing and pansharpening
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Nonrigid registration of hyperspectral and color images with vastly different spatial and spectral resolutions for spectral unmixing and pansharpening

机译：Hyperspectral和彩色图像的非防护和彩色图像，具有众异的空间和光谱分辨率，用于光谱解密和泛散

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摘要

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.
机译：在本文中，我们提出了一种通过利用点扩展功能（PSF）来注册具有非常大规模差异的图像的框架，并将其应用于注册高光谱和高分辨率彩色图像。该算法最小化具有结合的光谱响应函数（SRF）的最小二乘（LSQ）目标函数，在高光谱图像上应用于高光谱图像和彩色图像上的刚性变换的非抗原自由形变形。通过以交替的方式更新两个转换和两个物理函数来解决优化问题。我们通过将所提出的算法与其刚性变化的建议算法和基于两个相互信息的算法进行比较，在模拟Pavia大学数据集和真正的Salton Sea DataSet上执行了框架。结果表明，LSQ FreeForm版本具有非rigID仿真和实时数据集的最佳性能，具有少于0.15像素误差在高光谱域中给出了1个像素非误差失真。

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