[1]姚伏天,钱沄涛.高斯过程及其在高光谱图像分类中的应用[J].智能系统学报,2011,6(5):396-404.
　YAO Futian,QIAN Yuntao.Gaussian process and its applications in hyperspectral image classification[J].CAAI Transactions on Intelligent Systems,2011,6(5):396-404.
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高斯过程及其在高光谱图像分类中的应用

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 6 期数: 2011年第5期 页码: 396-404 栏目: 学术论文—人工智能基础 出版日期: 2011-10-30
Title:
Gaussian process and its applications in hyperspectral image classification
文章编号:
1673-4785(2011)05-0396-09
作者:
姚伏天1,2,钱沄涛1,2
1.浙江大学 计算机学院，浙江 杭州 310027；
2.浙江大学 人工智能研究所，浙江 杭州 310027
Author(s):
YAO Futian1,2， QIAN Yuntao1,2
1.College of Computer Science, Zhejiang University, Hangzhou 310027, China;
2.Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China
关键词:
高斯过程; 高光谱图像; 机器学习; 图像分类
Keywords:
Gaussian process;  hyperspectral imaging;  machine learning;  image classification
分类号:
TP181
文献标志码:
A
摘要:
高光谱遥感图像分类是高光谱成像信息处理的研究热点，高光谱成像的内在特点对于分类器设计具有直接影响.高斯过程是近年来发展迅速的一种新的机器学习方法，具备容易实现、超参数可自适应获取以及预测输出具有概率意义等优点，比较适合于处理图像分类问题.首先对高斯过程的基本概念及其主要的分类算法进行了简要介绍，然后在对高光谱图像分类的特点和高光谱图像分类的研究现状的分析基础上，讨论了基于高斯过程的高光谱图像分类的基本思想，提出了基于空间约束的高斯过程分类和基于半监督高斯过程分类等适合高光谱图像分类的新方法.最后对基于高斯过程的高光谱图像分类研究的发展趋势进行了展望.
Abstract:
Hyperspectral image classification is one of the hotspots in the field of remote sensing applications. The classification performance is affected by the inherit characteristics of hyperspectral imaging. Gaussian process (GP) is a recently developed machine learning method which enables explicitly probabilistic modeling and makes results easily interpretable. Furthermore, hyperparameters of GP can be learned from training data, which overcomes the difficulties of fixing model parameters in most classifiers. This paper introduced the basic concept of GP and some GPbased classification methods. After analyzing the characteristics of hyperspectral imaging and the existing classification methods for hyperspectral images, GP based classification for hyperspectral images was discussed, and some new GPbased classification methods such as GP with spatial constraints and semisupervised GP methods were proposed. Finally, several future research trends of GP and hyperspectral image classification were given.
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备注/Memo

收稿日期： 2010-10-19.
基金项目：国家自然科学基金资助项目（60872071）. 
通信作者：钱沄涛.E-mail:ytqian@zju.edu.cn.
作者简介：
姚伏天，男，1976年生，博士研究生，主要研究方向为模式识别、机器学习、高光谱成像信息处理，发表学术论文20余篇.
?钱沄涛，男，1968年生，教授，博士生导师，中国计算机学会人工智能与模式识别专业委员会委员、模糊逻辑与多值逻辑专业委员会委员.主要研究方向为模式识别、机器学习、信号处理，承担多项国家自然科学基金项目、国际合作基金项目和省部级重点科技项目，发表学术论文70余篇.

更新日期/Last Update: 2011-11-16
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