Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyperspectral Data

Yin J.; Gao C.; Jia X.

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Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyperspectral Data

机译：高光谱数据特征提取的小波包分析和灰色模型

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Wavelet packet analysis (WPA) and gray model (GM) are investigated for nonlinear unsupervised feature extraction of hyperspectral remote sensing data in this letter. Treated as derivative series, a hyperspectral response curve of each pixel is decomposed into an approximation and various detailed compositions by WPA, and then, GM is continuously applied to find the relationship among those detailed compositions. Cluster–space representation is used for determining the optimal wavelet. New extracted features can reveal the intrinsic identities of hyperspectral data. Experimental results show the feasibility and reliability of our proposed method in terms of classification accuracy.

机译：本文研究了小波包分析（WPA）和灰色模型（GM）用于高光谱遥感数据的非线性无监督特征提取。 WPA将每个像素的高光谱响应曲线视为导数级，将其分解为近似值和各种详细的成分，然后连续应用GM来查找这些详细成分之间的关系。群集空间表示法用于确定最佳小波。新提取的特征可以揭示高光谱数据的固有身份。实验结果证明了该方法在分类精度上的可行性和可靠性。

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《Geoscience and Remote Sensing Letters, IEEE》 |2013年第4期|p.682-686|共5页
作者
Yin J.; Gao C.; Jia X.;
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作者单位

School of Astronautics, Beihang University, Beijing, China;

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正文语种 eng
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关键词
Feature extraction; gray model (GM); hyperspectral response curve; separability factor (SF); wavelet packet analysis (WPA);

机译：特征提取;灰色模型（GM）;高光谱响应曲线;分离因子（SF）;小波包分析（WPA）;

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1. Application of the wavelet transform for feature extraction in the analysis of hyperspectral laser-induced fluorescence data [J] . Innokenti Sobolev, Sergey Babichenko International journal of remote sensing . 2013,第19a20期

机译：小波变换在特征提取中在高光谱激光诱导的荧光数据分析中的应用
2. An enhanced feature extraction model using lifting-based wavelet packet transform scheme and sampling-importance-resampling analysis [J] . Yixiang Huang, Chengliang Liu, Xuan F. Zha, Mechanical systems and signal processing . 2009,第8期

机译：利用基于提升的小波包变换方案和采样重要性重采样分析的增强特征提取模型
3. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform [J] . Renping Shao, Wentao Hu, Yayun Wang, Measurement . 2014,第Null期

机译：基于小波包变换的主成分分析和核主成分分析的齿轮故障特征提取与分类
4. Feature extraction of hyperspectral data using the Best Wavelet Packet Basis [C] . Pai-Hui Hsu, Yi-Hsing Tseng Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International . 2002

机译：使用最佳小波包基的特征提取高光谱数据
5. A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain. [D] . Mendenhall, Michael J. 2006

机译：用于从高光谱图像中提取特征的神经相关模型及其在小波域中的应用。
6. Wavelet based feature extraction and visualization in hyperspectral tissue characterization [O] . Martin Denstedt, Asgeir Bjorgan, Matija Milanič, 2014

机译：高光谱组织表征中基于小波的特征提取和可视化
7. Wavelet Packet Analysis Based Feature Extraction of Vehicular Acoustic Signal [O] . Qi Xiao-xuan 2016

机译：基于小波包分析的车载声信号特征提取
8. Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain [R] . Mendenhall, M. J. 2006

机译：高光谱图像特征提取的神经关联模型及其在小波域中的应用

Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyperspectral Data

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