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Advances in Unmixing of Hyperspectral Remote Sensing Imagery.

机译：高光谱遥感影像分解研究进展。

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摘要

Remote sensing technology has advanced tremendously in recent decades. An important driver for this development has been the offering of wide spatial and temporal coverage by space- and airborne platforms, as well as the ever-improving capability of their sensors to record images with high spatial and spectral resolution. A modality that produces a bulk of data for remote sensing is hyperspectral imaging. This modality records the reflected solar radiation in contiguous and often numerous spectral bands, thereby extending the standard photography by enabling to treat each pixel individually as a spectrum discernible for each class of materials. One limitation of such imaging, where the spatial and spectral resolutions are inherently traded against each other, is the occurrence of mixed pixels and spectral mixing. The unraveling of spectral mixtures has been widely studied as spectral unmixing, where two main aspects are of interest: the estimation of the constituent spectra, and of their fractions or abundances, in the mixture.;The work described in the thesis regards spectral unmixing from two objectives: advancement of methodology and introduction of unmixing in new applications. The first part, specifically, is concerned with the development of data-driven methods for spectral unmixing that can mitigate the dependency on physical parameters and models, and reduce high computational complexity due to the typical use of optimization techniques. A concrete realization consists of several algorithms that reformulate the known geometrical framework of spectral unmixing by introducing linear and nonlinear distance-based and analytical formulations. The second part introduces or elaborates spectral unmixing for detection of the atmospheric adjacency-effect and the estimation of quality of inland and coastal waters. The presented unmixing-based approaches in this context have been validated through theoretical and empirical comparison using available datasets and reference methods.
机译：近几十年来，遥感技术取得了巨大的进步。这一发展的重要推动力是空天和空降平台提供了广泛的空间和时间覆盖范围，以及其传感器以不断提高的能力以高空间和光谱分辨率记录图像的能力。产生大量遥感数据的方式是高光谱成像。这种方式可以在连续的光谱带（通常是许多光谱带）上记录反射的太阳辐射，从而通过将每个像素分别视为可识别每种材料的光谱来扩展标准摄影。这种成像的固有局限性在于空间分辨率和光谱分辨率相互抵触，这种局限性在于出现了混合像素和光谱混合现象。光谱混合物的拆解已作为光谱分解得到了广泛的研究，其中两个主要方面是令人关注的：混合物中组成光谱及其分数或丰度的估计。两个目标：方法的改进和在新应用程序中引入分解。第一部分特别涉及用于频谱分解的数据驱动方法的开发，该方法可减轻对物理参数和模型的依赖性，并由于优化技术的典型使用而降低了高计算复杂性。一个具体的实现由几种算法组成，这些算法通过引入基于线性和非线性距离的分析公式来重新构造光谱解混的已知几何框架。第二部分介绍或详细阐述了光谱分解技术，以检测大气邻近效应并估计内陆和沿海水域的质量。在这种情况下，所提出的基于混合的方法已经通过使用可用数据集和参考方法的理论和经验比较得到了验证。

著录项

  • 作者

    Burazerovic, Dzevdet.;

  • 作者单位

    Universiteit Antwerpen (Belgium).;

  • 授予单位 Universiteit Antwerpen (Belgium).;
  • 学科 Remote sensing.;Applied mathematics.;Electrical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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