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首页> 外文会议>IEEE 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：基于图形的半监督框架中的高光谱数据的小样本分类

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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 ?1norm graph to form a more discriminating graph called d?1graph. Then we combine this d?1graph with knn graph in proportion. The new graph can employ both spectral values and space information of hyperspectral data. We demonstrate the effectiveness of our proposal on the Indiana Pines hyperspectral data set and the results outperform state of the art.
机译：使用超细光谱遥感图像的光谱来识别和分类陆地覆盖，这是一个热门话题感谢其高分辨率频谱。然而，当标记样本的数量太小时，高光谱数据的分类精度将大大减少。大多数分类算法采取尺寸减少策略，并且需要丰富标记的样本，以便学习可以识别特定材料的分类器。但在大多数遥感情况下，标记样本是一个昂贵的任务，值得减少尺寸减少的有价值的信息。稀疏表示作为快速有效的算法的优点是，它可以与没有尺寸减少的小标记样品相当好。因此，我们提出了一个新的框架来构建基于图形的半监督模型，以解决标记样本的缺乏问题，并将K最近邻（knn）图组合起来，以实现空间特征的优势。在这个新模型中，稀疏表示用于通过估计属于同一类的成对像素来构建概率矩阵，并且该概率矩阵集成到？ 1 标准图中以形成更辨别的图表称为d？ 1 图。然后我们将此D？ 1 图与KNN图相结合。新图可以采用超光数据的光谱值和空间信息。我们展示了我们对印第安纳州的高光谱数据集的提案的有效性以及艺术率优先的结果。

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