高斯过程及其在高光谱图像分类中的应用

Gaussian process and its applications in hyperspectral image classification

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.

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 classi- fication 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.