Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction
Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction
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DOI：
10.1109/tgrs.2002.804721
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发表时间：
2002-10-01
影响因子：
8.2
通讯作者：
Li, J
中科院分区：
文献类型：
--
作者：
Bruce, LM;Koger, CH;Li, J
提出了一种基于二进离散小波变换的高维数据特征提取方法。小波的固有的多分辨率特性进行了讨论，在有关的多光谱和高光谱遥感。此外，各种基于小波的功能被应用到特定的地面植被从高光谱签名的自动分类的问题。使用自动统计分类器的小波变换功能进行评估。该系统使用高光谱数据进行测试，用于各种农业应用。实验结果表明，基于小波变换的特征具有良好的鉴别能力。自动分类系统始终提供超过95%和80%的分类准确度为端元和混合签名的应用程序，分别。与传统的特征提取方法相比，小波变换方法显着提高了整体分类精度。
In this paper, the dyadic discrete wavelet transform is proposed for feature extraction from a high-dimensional data space. The wavelet's inherent multiresolutional properties are discussed in terms related to multispectral and hyperspectral remote sensing. Furthermore, various wavelet-based features are applied to the problem of automatic classification of specific ground vegetations from hyperspectral signatures. The wavelet transform features are evaluated using an automated statistical classifier. The system is tested using hyperspectral data for various agricultural applications. The experimental results demonstrate the promising discriminant capability of the wavelet-based features. The automated classification system consistently provides over 95% and 80% classification accuracy for endmember and mixed-signature applications, respectively. When compared to conventional feature extraction methods, the wavelet transform approach is shown to significantly increase the overall classification accuracy.