Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields

Utsav B. Gewali, S. Monteiro
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引用次数: 4

Abstract

In this paper, we propose and compare two spectral angle based approaches for spatial-spectral classification. Our methods use the spectral angle to generate unary energies in a grid-structured Markov random field defined over the pixel labels of a hyperspectral image. The first approach is to use the exponential spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based function, with the support vector machine and the Gaussian process classifier. The second approach is to directly use the minimum spectral angle between the test pixel and the training pixels as the unary energy. We compare the proposed methods with the state-of-the-art Markov random field methods that use support vector machines and Gaussian processes with squared exponential kernel/covariance function. In our experiments with two datasets, it is seen that using minimum spectral angle as unary energy produces better or comparable results to the existing methods at a smaller running time.
基于光谱角的一元能量函数的空间光谱高光谱马尔可夫随机场分类
本文提出并比较了两种基于光谱角的空间光谱分类方法。我们的方法使用光谱角在高光谱图像的像素标签上定义的网格结构马尔可夫随机场中生成一元能量。第一种方法是使用指数谱角映射(ESAM)核/协方差函数,一个基于谱角的函数,结合支持向量机和高斯过程分类器。第二种方法是直接使用测试像素和训练像素之间的最小光谱角作为一元能量。我们将所提出的方法与使用支持向量机的最先进的马尔可夫随机场方法和具有平方指数核/协方差函数的高斯过程进行比较。在两个数据集的实验中,可以看到使用最小光谱角作为一元能量在更短的运行时间内产生比现有方法更好或可比较的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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