An endmember optimization approach for linear spectral unmixing of fine-scale urban imagery
An endmember optimization approach for linear spectral unmixing of fine-scale urban imagery
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DOI：
10.1016/j.jag.2013.09.013
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Jian Yang;Yuhong He;T. Oguchi
中科院分区：
文献类型：
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作者：
Jian Yang;Yuhong He;T. Oguchi
高空间分辨率影像的光谱分解在解释城市地表物质特征方面引起了越来越多的兴趣。本研究提出了一种基于端元空间分布（即立体角和四面体体积）的端元优化方法，以选择城市光谱解混的最佳端元组合。具体而言，在由图像的绿、红、近红外波段构成的合适的三维光谱空间中实现线性光谱分解模型（SESMA），并以立体角和四面体体积测量端元空间分布。发现立体角和四面体体积都与有效和正确的未混合比例有很强的线性或对数关系，而后者的测量也将光度阴影作为端元考虑在内。将基于端元优化方法的光谱解混结果与常用多端元光谱混合分析（MESMA）模型的解混结果进行了比较。对于不同的类，每个模型都有自己的优势。
Spectral unmixing of high spatial resolution imagery has attracted growing interest for interpreting urban surface material characteristics. This study proposes an endmember optimization method based on endmember spatial distribution (i.e. solid angle and tetrahedron volume) to select the optimal endmember combination for urban spectral unmixing. Specifically, a linear spectral unmixing model (SESMA) is implemented in a suitable 3-D spectral space structured by the green, red and near infrared bands of the imagery, and endmember spatial distribution is measured with solid angle and tetrahedron volume. Both the solid angle and tetrahedron volume are found to have a strong linear or logarithmic relationship with valid and correct unmixed proportions, whereas the latter measure also takes the photometric shade into account as an endmember. The spectral unmixing results based on the proposed endmember optimization method are compared with those from a common multiple endmember spectral mixture analysis (MESMA) model. Towards different classes, each model has its own advantages over the other.