Texture feature analysis using a Gauss-Markov model hyperspectral image classification
Texture feature analysis using a Gauss-Markov model hyperspectral image classification
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DOI：
10.1109/tgrs.2004.830170
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发表时间：
2004-07-01
影响因子：
8.2
通讯作者：
Zerubia, J
中科院分区：
文献类型：
--
作者：
Rellier, G;Descombes, X;Zerubia, J
纹理分析在单光谱和多光谱图像领域得到了广泛的研究。同时，设计了具有大量波段（超过10个）的新型图像传感器。它们能够提供精细光谱和空间采样的图像，被称为高光谱图像。这项工作的目的是在两个离散空间中进行联合纹理分析。为了实现这一目标，我们提出了一个概率矢量纹理模型，使用高斯-马尔可夫随机场（MRF）。MRF参数允许表征不同的高光谱纹理。这项工作的一个可能应用是对城市地区进行分类。这些区域不能仅用辐射测量法很好地表征，因此我们使用MRF参数作为最大似然分类算法中的新特征。机载可见/红外成像光谱仪高光谱图像的分析结果表明，在分析中加入纹理信息可以获得较好的分类效果。
Texture analysis has been widely investigated in the monospectral and multispectral imagery domains. At the same time, new image sensors with a large number of bands (more than ten) have been designed. They are able to provide images with both fine spectral and spatial sampling, and are called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, using a Gauss-Markov random field (MRF). The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas. These areas are not well characterized by radiometry alone, and so we use the MRF parameters as new features in a maximum-likelihood classification algorithm. The results obtained on Airborne Visible/Infrared Imaging Spectrometer hyperspectral images demonstrate that a better classification is achieved when texture information is included in the analysis.