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Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach.

机译：使用多光谱图像融合增强高光谱空间分辨率：一种小波方法。

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摘要

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.
机译：高光谱和空间分辨率的图像在遥感应用中具有重大影响。因为星载传感器的空间分辨率和光谱分辨率都是通过设计固定的，并且不可能进一步提高空间分辨率或光谱分辨率，所以必须应用诸如图像融合之类的技术来实现这些目标。为了提高高光谱图像的光谱和空间分辨率，本文引入了高光谱和多光谱传感器之间的小波融合概念。为了测试该概念的鲁棒性，首先对Hyperion（高光谱传感器）和Advanced Land Imager（多光谱传感器）中的图像进行配准，然后使用不同的小波算法进行融合。出于比较目的，还实施了基于回归的融合算法。结果表明，与地面真实情况相比，使用组合双线性小波回归算法的融合图像具有比其他方法更少的误差。另外，由于缺乏适当的配准，组合回归小波算法显示出对像素未对准的更大抵抗力。平均均方误差的定量度量表明，当高光谱图像的空间分辨率变为其对应的多光谱图像的空间分辨率的八倍时，基于小波的方法的性能会下降。无论采用哪种融合方法，图像融合的主要挑战都是图像配准，这也是一个非常耗时的过程。由于组合回归小波技术在计算上昂贵，因此还实现了基于回归和小波方法的混合技术以减少计算开销。但是，更快的计算收益被结果中引入更多误差所抵消。本文的第二个目的是研究未来NASA任务进行图像融合的可行性和传感器要求，以便能够执行机载图像融合。在此过程中，通过使用先前获取的数据的转换矩阵来注册输入图像，解决了图像注册的主要挑战。融合过程产生的合成图像与地面真实情况非常匹配，表明了实时机载融合处理的可能性。

著录项

  • 作者

    Jazaeri, Amin.;

  • 作者单位

    George Mason University.;

  • 授予单位 George Mason University.;
  • 学科 Remote Sensing.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 189 p.
  • 总页数 189
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遥感技术;
  • 关键词

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