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Hyperspectral Image Classification Through Bilayer Graph-Based Learning

机译:通过基于双层图的学习进行高光谱图像分类

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Hyperspectral image classification with limited number of labeled pixels is a challenging task. In this paper, we propose a bilayer graph-based learning framework to address this problem. For graph-based classification, how to establish the neighboring relationship among the pixels from the high dimensional features is the key toward a successful classification. Our graph learning algorithm contains two layers. The first-layer constructs a simple graph, where each vertex denotes one pixel and the edge weight encodes the similarity between two pixels. Unsupervised learning is then conducted to estimate the grouping relations among different pixels. These relations are subsequently fed into the second layer to form a hypergraph structure, on top of which, semisupervised transductive learning is conducted to obtain the final classification results. Our experiments on three data sets demonstrate the merits of our proposed approach, which compares favorably with state of the art.
机译:具有有限数量的标记像素的高光谱图像分类是一项艰巨的任务。在本文中,我们提出了一个基于双层图的学习框架来解决这个问题。对于基于图的分类,如何从高维特征建立像素之间的相邻关系是成功分类的关键。我们的图学习算法包含两层。第一层构造一个简单的图,其中每个顶点表示一个像素,边缘权重编码两个像素之间的相似度。然后进行无监督学习以估计不同像素之间的分组关系。这些关系随后被馈送到第二层以形成超图结构,在此之上,进行半监督的转导学习以获得最终的分类结果。我们在三个数据集上进行的实验证明了我们提出的方法的优点,与现有技术相比具有优势。

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