Kernel-based methods for hyperspectral image classification
Kernel-based methods for hyperspectral image classification
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DOI：
10.1109/tgrs.2005.846154
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发表时间：
2005-06-01
影响因子：
8.2
通讯作者：
Bruzzone, L
中科院分区：
文献类型：
--
作者：
Camps-Valls, G;Bruzzone, L
本文提出了基于核的方法在高光谱图像分类的背景下，从一般的角度说明了不同的基于核的方法的主要特点，并分析其在高光谱域的属性的框架。特别是，我们评估了正则化径向基函数神经网络（Reg-RBFNN），标准支持向量机（SVM），核Fisher判别（KFD）分析和正则化AdaBoost（Reg-AB）的性能。这项工作的新奇在于：1）引入Reg-RBFNN和Reg-AB用于高光谱图像分类; 2）考虑到高光谱图像的特殊性，比较基于核的方法; 3）澄清它们的理论关系。为了这些目的，我们专注于在嘈杂的环境中工作时，高输入维度和有限的训练集的方法的准确性。此外，一些其他重要的问题进行了讨论，如稀疏的解决方案，计算负担，和提供输出的方法，可以直接解释为概率的能力。
This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches and analyzing their properties in the hyperspectral domain. In particular, we assess performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (Reg-AB). The novelty of this work consists in: 1) introducing Reg-RBFNN and Reg-AB for hyperspectral image classification; 2) comparing kernel-based methods by taking into account the peculiarities of hyperspectral images; and 3) clarifying their theoretical relationships. To these purposes, we focus on the accuracy of methods when working in noisy environments, high input dimension, and limited training sets. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden,,and the capability of the methods to provide outputs that can be directly interpreted as probabilities.