An automated waveband selection technique for optimized hyperspectral mixture analysis
An automated waveband selection technique for optimized hyperspectral mixture analysis
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DOI：
10.1080/01431160903311305
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发表时间：
2010-10
影响因子：
3.4
通讯作者：
Ben Somers;S. Delalieux;W. Verstraeten;J. Aardt;G. Albrigo;P. Coppin
中科院分区：
文献类型：
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作者：
Ben Somers;S. Delalieux;W. Verstraeten;J. Aardt;G. Albrigo;P. Coppin
线性光谱混合分析 (SMA) 已广泛用于遥感研究中，以估计光谱混合的子像素组成。缺乏解释地面成分或端元光谱之间足够的时间和空间变化的能力已被认为是传统 SMA 方法的主要缺点。为了克服这个问题，提出并评估了一种新颖的自动化线性光谱混合协议，称为稳定区分离（SZU）。根据最小不稳定性指数 (ISI) 标准，自动选择稳定的光谱特征（即对光谱变异性最不敏感）用于混合物分析。 ISI 定义为混合物中存在的端元类内部的光谱变异性与端元类之间的光谱变异性之比。该算法在一组场景上进行了测试，这些场景是根据原位测量的高光谱数据生成的。这些场景涵盖了不同条件下的城市和自然环境。 SZU 提供了所有场景下可靠的端元覆盖分布图。平均而言，与传统 SMA 方法相比，R2（建模与观察到的子像素覆盖分数的决定系数）的绝对增益为 0.14，而分数丰度误差的绝对增益为 0.06。结论是，SZU 协议有潜力成为一种有效且高效的 SMA 算法，用于生成最佳覆盖率估计值，无论考虑何种场景。此外，SZU 实施的子集选择协议可被视为对传统 SMA 方法的补充，从而进一步减少光谱变异性。
Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing (SZU), is presented and evaluated. Stable spectral features (i.e. least sensitive to spectral variability) are automatically selected for use in the mixture analysis based on a minimum InStability Index (ISI) criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R2—the coefficient of determination of the modelled versus the observed sub-pixel cover fractions—of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.