多时相多角度高光谱遥感图像超分辨方法研究

This project will focus on multi-temporal and multi-angle superresolution reconstruction of hyperspectral remote sensing imagery. By making full advantage of the complementary information provided by the subtle sub-pixel shifts between a set of low resolution images of the same scene, superresolution has proved to be an efficient way of increasing the spatial resolution of target remote sensing imagery since it was first applied to multi-temporal LANDSAT 4 remote sensing data back in 1984. New opportunities for superresolution have occurred as an increasing number of recently available hyperspectral sensors suffer from low resolution but have the potential of increasing their resolution via superresolution techniques. For example,the Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS/Proba), which has the highest spatial resolution (18m) among all the space-borne hypersepctral sensors, are equipped with multi-angle capabilities providing multiple low resolution images suitable for superresolution image reconstruction. The main objective of this research is to develop practical and operationally applicable hyperspectral superresolution algorithms and procedures, while investigating the potential of superresolution to bridge the gap between high resolution airborne imagery and low resolution spaceborne imagery, based on a comprehensive invesitigation of the opportunities and challenges brought by hyperspectral remote sensing imagery.

本项目将关注于多时相多角度高光谱遥感图像的超分辨重建。通过利用多幅低分辨图像之间的亚像元差异，超分辨已经证实为一种有效地提高图像空间分辨率的手段。从1984年超分辨技术最先应用到LANDSAT 4卫星数据以来，超分辨技术在不同传感器数据上获得了广泛的应用。高光谱遥感影像的出现为超分辨技术提供了新的应用空间，这主要是因为高光谱影像一般具备很低的分辨率而高光谱传感器平台具有的多时相和多角度的特点往往使得超分辨重建成为可能。本项目的主要目标是在分析高光谱图像成像特点的基础上，研究高光谱图像对超分辨重建带来的挑战和机遇，开发一套实用的高光谱遥感影像超分辨重建的算法和程序，同时探讨高光谱超分辨重建对自动图像解译(图像分类和混合像元分解)的影响。

高光谱图像在遥感中越来越多地获得了应用,相对于传统的多光谱图像和全色图像，高光谱图像可以获取地物全面的光谱信息，正是由于高光谱图像具有的潜在应用价值，高光谱数据的获取引起了工业界和学术界的关注。但是高光谱影像相对于多光谱和全色影像，其空间分辨率低，从而制约了其应用。本项目针对该问题开展超分辨率研究。集中在点扩展函数自动估计和亚像元遥感影像配准两方面上进行了大量的研究工作。本项目提出了一种新的基于模型的点扩展函数估计方法和基于改进ASIFT的配准方法。在原型系统研发方面主要针对点扩展函数的估计设计了相应的算法实现模块，项目产生的成果在在遥感数据生产和遥感监测方面进行了应用。