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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Application and Evaluation of a Hierarchical Patch Clustering Method for Remote Sensing Images
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Application and Evaluation of a Hierarchical Patch Clustering Method for Remote Sensing Images

机译：遥感影像分层补丁聚类方法的应用与评价

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In this paper, we apply and evaluate a modified Gaussian-test-based hierarchical clustering method for high-resolution satellite images. The purpose is to obtain homogeneous clusters within each hierarchy level which later allow the classification and annotation of image data ranging from single scenes up to large satellite data archives. After cutting a given image into small patches and feature extraction from each patch, $k$-means are used to split sets of extracted image feature vectors to create a hierarchical structure. As image feature vectors usually fall into a high-dimensional feature space, we test different distance metrics, to tackle the “curse of dimensionality” problem. By using three different synthetic aperture radar (SAR) and optical image datasets, Gabor texture and Bag-of-Words (BoW) features are extracted, and the clustering results are analyzed via visual and quantitative evaluations. We also compared our approach with other classic unsupervised clustering methods. The most important contributions of this paper are the discussion and evaluation of cluster homogeneity by comparing various datasets, feature descriptors, evaluation measures, and clustering methods, as well as the analysis of the clustering performances under various distance metrics. The results show that the Gaussian-test-based hierarchical patch clustering method is able to obtain homogeneous clusters, while Gabor texture features perform better than the BoW features. In addition, it turns out that a distance parameter ranging from 1.2 to 2 performs best. Also indicated by [1], our modified G-means algorithm is faster than the original algorithm.
机译：在本文中，我们对高分辨率卫星图像应用并评估了一种改进的基于高斯检验的分层聚类方法。目的是获得每个层次结构级别内的同质群集，随后可以对图像数据进行分类和注释，范围从单个场景到大型卫星数据档案。将给定的图像切成小块并从每个块中提取特征后，使用$ k $ -means拆分提取的图像特征向量集，以创建分层结构。由于图像特征向量通常属于高维特征空间，因此我们测试了不同的距离度量，以解决“维数诅咒”问题。通过使用三个不同的合成孔径雷达（SAR）和光学图像数据集，提取了Gabor纹理和单词袋（BoW）特征，并通过视觉和定量评估分析了聚类结果。我们还将我们的方法与其他经典的无监督聚类方法进行了比较。本文最重要的贡献是通过比较各种数据集，特征描述符，评估方法和聚类方法以及对各种距离度量下的聚类性能进行分析来讨论和评估聚类的同质性。结果表明，基于高斯检验的分层补丁聚类方法能够获得同质聚类，而Gabor纹理特征的性能优于BoW特征。此外，事实证明，距离参数在1.2到2之间表现最佳。同样由[1]表示，我们的改进的G均值算法比原始算法更快。

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