Spatial/spectral endmember extraction by multidimensional morphological operations
Spatial/spectral endmember extraction by multidimensional morphological operations
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DOI：
10.1109/tgrs.2002.802494
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发表时间：
2002-09-01
影响因子：
8.2
通讯作者：
Plaza, J
中科院分区：
文献类型：
--
作者：
Plaza, A;Martínez, P;Plaza, J
光谱混合分析为遥感多维图像的解译和分类提供了一种有效的机制。它的目的是识别一组参考签名（也称为端元），可用于模拟原始图像每个像素的反射光谱。因此，建模被执行为有限数量的地面分量的线性组合。虽然光谱混合模型已被证明是适当的大型高光谱数据集亚像素分析的目的，在文献中提取适当的端元的方法很少;在光谱解混。大多数方法都是从光谱学的角度设计的，因此往往忽略像素之间现有的空间相关性。本文提出了一种新的自动化方法，执行无监督的像素纯度确定和端元提取多维数据集，这是通过使用空间和光谱信息相结合的方式来实现。该方法是基于数学形态学，一个经典的图像处理技术，可以应用到光谱域，同时能够保持其空间特性。建议的方法进行评估，通过一个专门设计的框架，使用模拟和真实的高光谱数据。
Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers; in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.