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首页> 外文期刊>Journal of Applied Remote Sensing >Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery
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Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery

机译：自适应支持向量机和马尔可夫随机场模型用于高光谱图像分类

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摘要

Markov random field (MRF) provides a useful model for integrating contextual information into remote sensing image classification. However, there are two limitations when using the conventional MRF model in hyperspectral image classification. First, the maximum likelihood classifier used in MRF to estimate the spectral-based probability needs accurate estimation of covariance matrix for each class, which is often hard to obtain with a small number of training samples for hyperspectral imagery. Second, a fixed spatial neighboring impact parameter for all pixels causes overcorrection of spatially high variation areas and makes class boundaries blurred. This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery. An adaptive spatial neighboring impact parameter is assigned to each pixel according to its spatial contextual correlation. Experimental results of a hyperspectral image show that the classification accuracy from the proposed method has been improved compared to those from the conventional MRF model and pixel-wise classifiers including the maximum likelihood classifier and SVM classifier.
机译：马尔可夫随机场（MRF）为将上下文信息集成到遥感图像分类中提供了有用的模型。但是，在高光谱图像分类中使用常规MRF模型时有两个限制。首先，在MRF中用于估计基于频谱的概率的最大似然分类器需要针对每个类别进行协方差矩阵的准确估计，而对于少量的高光谱图像训练样本而言，这通常很难获得。其次，所有像素的固定空间邻近冲击参数会导致空间上高变化区域的过度校正，并使类边界模糊。本文提出了一种集成支持向量机（SVM）和马尔可夫随机场以对高光谱图像进行分类的改进方法。根据每个像素的空间上下文相关性，为每个像素分配一个自适应的空间相邻影响参数。高光谱图像的实验结果表明，与传统的MRF模型和包括最大似然分类器和SVM分类器的像素分类器相比，该方法的分类精度有所提高。

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