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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?
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Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?

机译：城市环境中亚像素植被部分的光谱混合分析：如何纳入端成员变异性？

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In the urban environment both quality of life and surface biophysical processes are closely related to the presence of vegetation. Spectral mixture analysis (SMA) has been frequently used to derive subpixel vegetation information from remotely sensed imagery in urban areas, where the underlying landscapes are assumed to be composed of a few fundamental components, called endmembers. A critical step in SMA is to identify the endmembers and their corresponding spectral signatures. A common practice in SMA assumes a constant spectral signature for each endmember. In fact, the spectral signatures of endmembers may vary from pixel to pixel due to changes in biophysical (e.g. leaves, stems and bark) and biochemical (e.g. chlorophyll content) composition. This study developed a Bayesian Spectral Mixture Analysis (BSMA) model to understand the impact of endmember variability on the derivation of subpixel vegetation fractions in an urban environment. BSMA incorporates endmember spectral variability in the unmixing process based on Bayes Theorem. In traditional SMA, each endmember is represented by a constant signature, while BSMA uses the endmember signature probability distribution in the analysis. BSMA has the advantage of maximally capturing the spectral variability of an image with the least number of endmembers. In this study, the BSMA model is first applied to simulated images, and then to Ikonos and Landsat ETM+ images. BSMA leads to an improved estimate of subpixel vegetation fractions, and provides uncertainty information for the estimates. The study also found that the traditional SMA using the statistical means of the signature distributions as endmember signatures produces subpixel endmember fractions with almost the same and sometimes even better accuracy than those from BSMA except without uncertainty information for the estimates. However, using the modes of signature distributions as endmembers may result in serious bias in subpixel endmember fractions derived from traditional SMA. (c) 2005 Elsevier Inc. All rights reserved.
机译：在城市环境中，生活质量和表面生物物理过程都与植被的存在密切相关。光谱混合分析（SMA）经常用于从城市地区的遥感影像中获取亚像素植被信息，在该地区中，潜在的景观被假定为由一些基本成分组成，称为末端构件。 SMA的关键步骤是识别端基及其相应的光谱特征。 SMA的一种常见做法是假设每个端成员具有恒定的光谱特征。实际上，由于生物物理（例如，叶，茎和树皮）和生化（例如，叶绿素含量）组成的变化，末端成员的光谱特征可能因像素而异。这项研究开发了贝叶斯光谱混合分析（BSMA）模型，以了解末端成员变异性对城市环境中亚像素植被分数的推导的影响。 BSMA在基于贝叶斯定理的解混过程中将端成员光谱可变性纳入其中。在传统的SMA中，每个终端成员都由恒定的签名表示，而BSMA在分析中使用终端成员签名的概率分布。 BSMA的优点是最大程度地捕获了具有最少端成员数量的图像的光谱变异性。在这项研究中，首先将BSMA模型应用于模拟图像，然后应用于Ikonos和Landsat ETM +图像。 BSMA改进了对亚像素植被分数的估计，并为估计提供了不确定性信息。该研究还发现，传统的SMA使用签名分布的统计方法作为端成员签名，所产生的子像素端成员分数与BSMA几乎相同，有时甚至比来自BSMA的精度更高，除非没有估计的不确定性信息。然而，使用特征分布的模式作为末端成员可能会导致源自传统SMA的子像素末端成员分数的严重偏差。 （c）2005 Elsevier Inc.保留所有权利。

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