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首页> 外文会议>International Geoscience and Remote Sensing Symposium >Small-sample classification of hyperspectral data in a graph-based semi-supervision framwork
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Small-sample classification of hyperspectral data in a graph-based semi-supervision framwork

机译：基于图的半监督框架中的高光谱数据小样本分类

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Using spectra of hyperspectral remote sensing imagery to identify and classify land cover has been a hot topic thanks for its high resolution spectrum. However, when the quantity of labeled samples is too small, the classification accuracy of hyperspectral data will be reduced greatly. Most classification algorithms take dimensional reduction strategy and require plentiful labeled samples in order to learn the classifier that could then recognize a specific material. But in most remote sensing situations labeling samples is a costly task and the valued information would be lost with dimensional reduction. Sparse representation as a fast and effective algorithm has the advantages that it can perform quite well with small labeled samples without dimensional reduction. Therefore, we proposed a new framework to construct a graph-based semi-supervised model to solve paucity problem of labeled samples and combine the k nearest neighbor (knn) graph to take the advantage of space features. In this new model, sparse representation is used to build the probability matrix by estimating if a pairwise pixels belonging to the same class, and this probability matrix is integrated into ℓ
机译：由于其高分辨率的光谱，使用高光谱遥感影像的光谱来识别和分类土地覆被一直是一个热门话题。然而，当标记样品的数量太少时，高光谱数据的分类精度将大大降低。大多数分类算法都采用降维策略，并且需要大量带标签的样本，以便学习可以识别特定材料的分类器。但是，在大多数遥感情况下，给样品加标签是一项昂贵的工作，而有价值的信息会因尺寸减小而丢失。稀疏表示是一种快速有效的算法，它的优点是可以在不缩小尺寸的情况下使用较小的标记样本很好地执行。因此，我们提出了一个新的框架来构建基于图的半监督模型，以解决标记样本的稀缺性问题，并结合k最近邻（knn）图以利用空间特征。在这个新模型中，稀疏表示用于通过估计是否属于同一类的成对像素来建立概率矩阵，并且将该概率矩阵集成到ℓ中。

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