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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features
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Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features

机译：利用改进的高斯马尔可夫随机场纹理特征对高空间分辨率影像进行分类

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摘要

Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependence of neighboring pixels within a texture to produce features. In this paper, neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and a step-by-step least squares method is proposed to extract a novel set of GMRF texture features, named as PS-GMRF. A complete procedure is first designed to classify texture samples of QuickBird imagery. After texture feature extraction, a subset of PS-GMRF features is obtained by the sequential floating forward-selection method. Then, the maximum a posterior iterated conditional mode classification algorithm is used, involving the selected PS-GMRF texture features in combination with spectral features. The experimental results show that the performance of classifying texture samples on high spatial resolution QuickBird satellite imagery is improved when texture features and spectral features are used jointly, and PS-GMRF features have a higher discrimination power compared to the classical GMRF features, making a notable improvement in classification accuracy from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features—the lowest order variance—is effective for residential-area detection. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variance with spectral features improves the classification accuracy compared to classification with purely spectral features.
机译：高斯马尔可夫随机场（GMRF）用于分析纹理。 GMRF测量纹理内相邻像素的相互依赖性以产生特征。在本文中，根据相邻像素与中心像素的距离，按优先级顺序考虑了相邻像素，并提出了一种逐步最小二乘方法来提取一组新的GMRF纹理特征，称为PS-GMRF 。首先设计一个完整的过程来对QuickBird影像的纹理样本进行分类。提取纹理特征后，通过顺序浮动前向选择方法获得PS-GMRF特征的子集。然后，最大程度地使用后验迭代条件模式分类算法，该算法涉及选定的PS-GMRF纹理特征与光谱特征的组合。实验结果表明，结合使用纹理特征和光谱特征，可以提高高分辨率空间QuickBird卫星图像纹理分类的性能，并且与传统的GMRF特征相比，PS-GMRF特征具有更高的辨别力。分类准确度从71.84％提高到94.01％。另一方面，发现PS-GMRF纹理特征之一（最低阶变化）对居住区检测有效。 IKONOS和SPOT-5图像的一些结果表明，与仅具有光谱特征的分类相比，将最低阶方差与光谱特征进行集成可以提高分类精度。

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