Unsupervised feature extraction based on a mutual information measure for hyperspectral image classification
Unsupervised feature extraction based on a mutual information measure for hyperspectral image classification
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DOI：
10.1109/igarss.2011.6049567
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Md. Ali Hossain;M. Pickering;X. Jia
中科院分区：
文献类型：
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作者：
Md. Ali Hossain;M. Pickering;X. Jia
从高维空间中寻找最具信息量的特征用于可靠的类数据建模是高光谱图像分类中最具挑战性的问题之一。这个问题可以使用两种基本技术来解决：特征选择和特征提取。主成分分析(PCA)是目前最流行的特征提取方法之一，但它的成分并不总是适合分类。本文提出了一种特征约简方法(MI-PCA)，该方法对通过主成分分析得到的分量进行非参数互信息(MI)度量。使用高光谱数据集的监督分类结果证实，新的MI-PCA技术通过选择比在原始数据上使用PCA或MI时更相关的特征来提供更好的分类精度。
Finding the most informative features from high dimensional space for reliable class data modeling is one of the most challenging problems in hyperspectral image classification. The problem can be address using two basic techniques: feature selection and feature extraction. One of the most popular feature extraction methods is Principal Component Analysis (PCA), however its components are not always suitable for classification. In this paper, we present a feature reduction method (MI-PCA) which uses a nonparametric mutual information (MI) measure on the components obtained via PCA. Supervised classification results using a hyperspectral data set confirm that the new MI-PCA technique provides better classification accuracy by selecting more relevant features than when using either PCA or MI on the original data.