Hyperspectral image classification by steepest ascent relevance vector machine

DONG Chao; TIAN Lian-fang

doi:10.3788/OPE.20122006.1398

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Hyperspectral image classification by steepest ascent relevance vector machine

更新时间：2020-08-12

- Hyperspectral image classification by steepest ascent relevance vector machine
- Optics and Precision Engineering Vol. 20, Issue 6, Pages: 1398-1405(2012)
- 作者机构：
  
  华南理工大学自动化科学与工程学院,广东广州,510640
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122006.1398
  CLC： TP751.1
- Received：09 February 2012，
  
  Revised：10 March 2012，
  
  Published Online：10 June 2012，
  
  Published：10 June 2012
- 稿件说明：
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董超, 田联房. 最速上升关联向量机高光谱影像分类[J]. 光学精密工程, 2012,20(6): 1398-1405

DONG Chao, TIAN Lian-fang. Hyperspectral image classification by steepest ascent relevance vector machine[J]. Editorial Office of Optics and Precision Engineering, 2012,20(6): 1398-1405
董超, 田联房. 最速上升关联向量机高光谱影像分类[J]. 光学精密工程, 2012,20(6): 1398-1405 DOI： 10.3788/OPE.20122006.1398.

DONG Chao, TIAN Lian-fang. Hyperspectral image classification by steepest ascent relevance vector machine[J]. Editorial Office of Optics and Precision Engineering, 2012,20(6): 1398-1405 DOI： 10.3788/OPE.20122006.1398.

摘要

针对高光谱影像近邻波段高度相关

直接在高维空间分类并非最优的问题

提出了基于最速上升和关联向量机(SA-RVM)的高光谱影像分类算法。使用最速上升(SA)算法搜索最优特征子空间

剔除冗余特征;然后

在特征子空间中训练RVM并分类。对4套测试数据进行的实验表明

SA选择的特征子空间中

RVM分类精度提高了2.5%以上

与支持向量机(SVM)相当。对训练样本较少的2套数据

精度提高了5.63%和6.2%。此外

SA-RVM的解稀疏

预测未知样本类别属性所需时间短。总体来看

SA-RVM精度高、判别速度快

适合处理大场景高光谱影像。

Abstract

As the adjacent bands of a hyperspectral image are highly correlated

it is not optimum to classify the hyperspectral image in the high dimensional space. To solve the problem

a novel hyperspectral image classifier based on Steepest Ascent and Relevance Vector Machine (SA-RVM) was proposed in this paper. The SA was used to search an optimum feature space and to eliminate redundant features of the image firstly. Then

RVM was trained in the optimized feature subspace and used to classify the test samples. Experiments were performed for four sets of data

it is shown that the accuracies of RVM have raised more than 2.5% in the feature subspace selected by SA

which is close to those of Support Vector Machines(SVMs). For the two data sets with fewer training samples

the accuracies of RVM increase by 5.63% and by 6.2% in the subspace. In addition

benefiting from the sparse solution

the SA-RVM requires very short time in predicting the class labels of unknown sam ples. It concludes that the SA-RVM has higher precision and efficiency in the prediction

and it is suitable for processing the large-scale hyperspectral images.

关键词

Keywords

references

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董超, 赵慧洁. 关联向量机在高光谱影像分类中的应用[J]. 遥感学报, 2010,14(6):1279-1284. DONG CH, ZHAO H J. Hyperspectral image classification and application based on relevance vector machine [J]. Journal of Remote Sensing, 2010,14(6):1279-1284.(in Chinese)

董超, 田联房, 赵慧洁. 遗传关联向量机高光谱影像分类[J]. 上海交通大学学报, 2011,45(10):1516-1520. DONG CH, TIAN L F, ZHAO H J. Hyperspectral image classification by genetic relevance vector machine [J]. Journal of Shanghai Jiao Tong University, 2011,45(10):1516-1520.(in Chinese)

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