Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?
Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?
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DOI：
10.1016/j.rse.2005.01.002
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发表时间：
2005-03-30
影响因子：
13.5
通讯作者：
Song, CH
中科院分区：
文献类型：
--
作者：
Song, CH
在城市环境中，生活质量和地表生物物理过程都与植被的存在密切相关。光谱混合分析（SMA）已被频繁地用于从城市地区的遥感图像中获得亚像素植被信息，其中底层景观被假定为由几个基本组成部分组成，称为端元。SMA的关键步骤是识别端元及其相应的光谱特征。SMA中的常见做法假定每个端元的光谱特征恒定。事实上，端元的光谱特征可能因像素而异，这是由于生物物理（例如叶、茎和树皮）和生物化学（例如叶绿素含量）组成的变化。本研究开发了一个贝叶斯光谱混合分析（BSMA）模型，以了解在城市环境中的亚像素植被分数的推导端元变异的影响。BSMA在基于贝叶斯定理的解混过程中引入了端元光谱变异性。在传统的SMA中，每个端成员由一个常数签名表示，而BSMA在分析中使用端成员签名概率分布。BSMA的优点是最大限度地捕捉光谱变化的图像与最少数量的端元。在这项研究中，BSMA模型首先应用于模拟图像，然后Ikonos和Landsat ETM+图像。BSMA导致改进的亚像素植被分数的估计，并提供不确定性信息的估计。该研究还发现，传统的SMA使用的签名分布的统计手段作为端元签名产生亚像素端元分数几乎相同，有时甚至更好的精度比那些从BSMA除了没有不确定性信息的估计。然而，使用的模式的签名分布作为端元可能会导致严重的偏差，从传统的SMA亚像素端元分数。(c)2005年爱思唯尔公司All rights reserved.
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.