最速上升关联向量机高光谱影像分类

董超; 田联房

doi:10.3788/OPE.20122006.1398

您当前的位置：

首页 >

文章列表页 >

最速上升关联向量机高光谱影像分类

信息科学 | 更新时间：2020-08-12

- 最速上升关联向量机高光谱影像分类
- Hyperspectral image classification by steepest ascent relevance vector machine
- 光学精密工程 2012年20卷第6期页码：1398-1405
- 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：
  
  中国博士后科学基金面上资助项目(No.20100480750)();中央高校基本科研业务费资助项目(No.2012ZM0100)()
- DOI：10.3788/OPE.20122006.1398
  中图分类号： TP751.1
- 收稿日期：2012-02-09，
  
  修回日期：2012-03-10，
  
  网络出版日期：2012-06-10，
  
  纸质出版日期：2012-06-10
- 稿件说明：
移动端阅览
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

HUGHES G F. On the mean accuracy of statistical pattern recognizers [J]. IEEE Transactions on Information Theory, 1968,14(1):55-63.

TADJUDIN S, LANDGREBE D A. Covariance estimation with limited training samples [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37(4):2113-2118.

KUO B C, LANDGREBE D A. Nonparametric weighted feature extraction for classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(5):1096-1105.

SERPICO S B, BRUZZONE L. A new search algorithm for feature selection in hyperspectral remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(7):1360-1367.

DUNDAR M M, LANDGREBE D A. A cost-effective semisupervised classifier approach with kernels [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(1):264-270.

MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(8):1778-1790.

BAZI Y, MELGANI F. Toward an optimal SVM classification system for hyperspectral remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(11):3374-3385.

TIPPING M E. Sparse bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001, 1:211-244.

DEMIR B, ERTURK S. Hyperspectral image classification using relevance vector machines [J]. IEEE GeoScience and Remote Sensing Letters, 2007,4(4):586-590.

FOODY G M. RVM-based multi-class classification of remotely sensed data [J]. International Journal of Remote Sensing, 2008,29(6):1817-1823.

CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al.. Retrieval of oceanic chlorophyll concentration with relevance vector machines [J]. Remote Sensing of Environment, 2006,105:23-33.

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)

AVIRIS N W. Indiana’s Indian Pines Data Set .ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C, 1992.

浏览量

266

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

3D-CNN与Transformer混合结构的高光谱图像空谱联合分类

CNN-Transformer结合对比学习的高光谱与LiDAR数据协同分类

同质区共享端元变异性的高光谱混合像元分解

利用光谱解混合的目标检测

面向领域自适应的部分最优传输高光谱图像分类