Hyperspectral Rock Spectral Classification Based on the Decision Tree-Support Vector Machine(DT-SVMs)

WANG Zhenghai; FAN Chen; HE Fengping; ZU Yuchuan; WANG Lei; WANG Juan

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Hyperspectral Rock Spectral Classification Based on the Decision Tree-Support Vector Machine(DT-SVMs)

更新时间：2023-12-11

- Hyperspectral Rock Spectral Classification Based on the Decision Tree-Support Vector Machine(DT-SVMs)
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 53, Issue 6, Pages: 93-97(2014)
- 作者机构：
  
  1. 中山大学地球科学系,广东,广州,510275
  2. 
  3. 广东省地质过程与矿产资源探查重点实验室,广东,广州,510275
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014，
  
  Published Online：25 November 2014，
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WANG Zhenghai, FAN Chen, HE Fengping, et al. Hyperspectral Rock Spectral Classification Based on the Decision Tree-Support Vector Machine(DT-SVMs). [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 53(6):93-97(2014)
DOI：

WANG Zhenghai, FAN Chen, HE Fengping, et al. Hyperspectral Rock Spectral Classification Based on the Decision Tree-Support Vector Machine(DT-SVMs). [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 53(6):93-97(2014) DOI：

摘要

地面岩性波谱建模是高光谱遥感地质填图的关键环节之一，地面岩性波谱具有高维、多类特点，传统的多光谱分类方法不能对地面岩性高光谱数据进行有效处理。该文在岩性波谱特征分析的基础上，构建岩性波谱特征空间；然后，从支持向量机(SVM)基本理论出发并对算法改进，建立了一个基于决策树的SVM多类分类器（DT-SVMs）；最后，利用云南北衙金矿区采集的野外实测岩性波谱数据进行分类实验，分类正确率达到93.75%。实验结果表明：基于决策树的多类分类支持向量机(DT-SVMs)可以很好的应用于地面岩性的波谱分类建模。同时，可推广到高光谱遥感岩性分类研究。

Abstract

The methods of multispectral imagery processing

such as artificial neural network classification and independent component analysis are not applicable to high spectral imagery

because of “Hughes Phenomenon” (when training samples are fixed

the classification accuracy decreases with the increase of feature dimension) and classification accuracy under small study samples can not be effectively solved for high dimensional data. A decision-tree-based multiclass support vector machines is proposed and applied in spectral classification for the multi-classification problem of surface rocks collected from the Beiya gold mine

Heqing County

Yunnan Province. The results show that the average classification accuracy rate can be above 93.75%

suggesting that multiple classification based on decision tree classification support vector machine (SVM) can be applied to spectral classification of rocks.

关键词

岩性波谱特征提取支持向量机多类分类决策树

Keywords

hyperspectral rock classificationfeature extractionsupport vector machine (SVM)multi-class classificationdecision tree

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Related Institution

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

Department of Mathematics,Sun Yatsen University

佛山科学技术学院计算机系，广东佛山 528000

华中科技大学计算机学院

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