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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines
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Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines

机译：使用数学形态学和支持向量机对超高分辨率图像进行分类

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摘要

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.
机译：我们使用亚度量全色图像调查了形态算子对城市场景中土地利用分类的相关性。支持向量机用于分类。已经使用了六种类型的过滤器：打开和关闭，通过重构打开和关闭以及打开和关闭礼帽。讨论了过滤器的类型和规模，并应用了一种称为递归特征消除的特征选择算法来减小输入数据的维数。对两张QuickBird全色图像进行的分析表明，在如此高的空间分辨率下，简单的打开和关闭操作符与分类最相关。此外，结合了简单滤波器和重构滤波器的混合集可提供最佳性能。在具有不同建筑风格特征的区域的两幅图像上进行的测试，对于特征选择和分类准确性均产生了相似的结果，表明突出显示的特征集具有普遍性。

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