Robust support vector method for hyperspectral data classification and knowledge discovery
Robust support vector method for hyperspectral data classification and knowledge discovery
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DOI：
10.1109/tgrs.2004.827262
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发表时间：
2004-07-01
影响因子：
8.2
通讯作者：
Moreno, J
中科院分区：
文献类型：
--
作者：
Camps-Valls, G;Gómez-Chova, L;Moreno, J
在本文中，我们提出了使用支持向量机（SVM）的自动高光谱数据分类和知识发现。在研究的第一阶段，我们使用支持向量机的作物分类和分析其性能的效率和鲁棒性，广泛使用的神经和模糊方法相比。通过评估几个场景中的准确性和统计差异来评估效率。鲁棒性分析方面：1）适用于工作条件时的特征选择阶段是不可能的，2）性能时，不同水平的高斯噪声引入其输入。在这项工作的第二阶段，我们分析的支持向量（SV）的分布，并进行灵敏度分析的最佳分类器，以分析输入光谱波段的意义。为了进行分类，使用了在DAISEX-1999活动期间用128波段HyMAP光谱仪获得的六幅高光谱图像。为每个图像标记六个作物类。一组精简的标记样本用于训练模型，整个图像用于评估其性能。得出了若干结论：1）支持向量机在准确性、简单性和鲁棒性方面比神经网络产生更好的结果; 2）当使用高维输入空间和大量训练数据时，训练神经和神经模糊模型是不可行的; 3）支持向量机对具有不同输入维度的不同训练子集的表现相似，这表明成功地检测了噪声频带;以及4）通过灵敏度分析实现波段的有价值的排序。
In this paper, we propose the use of support vector machines (SVMs) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of: 1) suitability to working conditions when a feature selection stage is not possible and 2) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SVs) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labeled for each image. A reduced set of labeled samples is used to train the models, and the entire images are used to assess their performance. Several conclusions are drawn: 1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity, and robustness; 2) training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data; 3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and 4) a valuable ranking of bands through sensitivity analysis is achieved.