| 基于独立成分分析和神经网络的高光谱遥感数据分类 |
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| 引用本文： | 宋江红,冯燕.基于独立成分分析和神经网络的高光谱遥感数据分类[J].遥感技术与应用,2006,21(2):115-119. |
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| 作者姓名： | 宋江红 冯燕 |
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| 作者单位： | ( 西北工业大学电子信息学院, 陕西 西安 710072) |
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| 摘 要： | 高光谱遥感数据以数据量大、含混度高、地面样本数据少的特点给分类处理带来了困难。将独立成分分析技术与多层前向神经网络相结合, 得到一种新的分类算法。独立成分分析在提取有效光谱特征的同时, 大大降低了数据的维数。神经网络作为分类器, 分类精度显著高于传统的bayes 分类器。通过对220 波段的高光谱数据进行实验, 得到了良好的效果。
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| 关 键 词： | 高光谱 分类 独立成分分析 特征提取 神经网络 |
| 文章编号： | 1004-0323（2006）02-0115-05 |
| 收稿时间： | 2005-10-25 |
| 修稿时间： | 2006-02-13 |
Hyperspectral Data Classification by Independent Component Analysis and Neural Network |
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| SONG Jiang-hong,FENG Yan.Hyperspectral Data Classification by Independent Component Analysis and Neural Network[J].Remote Sensing Technology and Application,2006,21(2):115-119. |
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| Authors: | SONG Jiang-hong FENG Yan |
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| Affiliation: | ( Institute of Electronic Information, Northwestern Polytechnical University , X i'an 710072, China) |
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| Abstract: | The basic premise of hyperspectral data classification is that the spectral features of various materials are different from each other.With the development of sensor technology,hyperspectral sensor can collect in as many as several hundreds spectral bands at once.The hyperspectral data,which features high spectral resolution,also brings great difficulties to classification.Because of the high dimension of data,great confusion and limited samples,the performance of traditional algorithms in hyperspectral data classification is deteriorated.In this paper a new combined algorithm based on independent component analysis(ICA) and neural network is proposed.Firstly,the independent component analysis algorithm is used for feature abstraction.The spectral features are assumed to be a linear mixture of constituent spectra from the material types.The independent components are informative for classification,meanwhile the number of independent components is much smaller than the bands of original data.Then the multi-layer feed-forward neural network is used for classification.The spectral features of each pixel are used to be the input vector of neural network.The neural network is trained by Levenberg-Marquardt back-propagation(LMBP) algorithm.The classification precision is remarkably superior to that of the conventional bayes classsifier.It proves by the experiment upon the 220-band hyperspectral data that the performance of this combined algorithm is satisfying. |
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| Keywords: | Hyperspectral Classification Independent component analysis Feature abstraction Neural network |
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