Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification
Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification
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DOI：
10.1016/j.patcog.2018.03.027
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Yuanjie Shao;N. Sang;Changxin Gao;Li Ma
中科院分区：
文献类型：
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作者：
Yuanjie Shao;N. Sang;Changxin Gao;Li Ma
在基于图的半监督分类方法中，构造一个能够捕捉到图像内在数据结构的图是一个关键问题，而基于稀疏表示的方法在半监督分类中表现出了良好的性能.然而，大多数基于SR的算法未能考虑HSI丰富的空间信息，这已被证明是有益的分类任务，在本文中，我们提出了一个空间和类结构正则化稀疏表示（SCSSR）图的半监督HSI分类。具体地说，通过图拉普拉斯正则化将空间信息引入到SR模型中，假设空间邻域具有相似的表示系数，得到的系数矩阵能够更准确地反映样本之间的相似性。此外，我们还将概率类结构，这意味着每个样本和每个类之间的概率关系，到SR模型，以进一步提高图的可辨别性.在ECONOMIC和AVIRIS高光谱数据上的实验结果表明，我们的方法优于最先进的方法。
Constructing a good graph that can capture intrinsic data structures is critical for graph-based semi-supervised learning methods, which are widely applied for hyperspectral image (HSI) classification with small amount of labeled samples.Among the existing graph construction methods, sparse representation (SR)-based methods have shown impressive performance on semi-supervised HSI classification tasks. However, most SR-based algorithms fail to consider the rich spatial information of HSI, which has been shown beneficial for classification tasks.In this paper, we propose a spatial and class structure regularized sparse representation (SCSSR) graph for semi-supervised HSI classification. Specifically, spatial information has been incorporated into SR model via the graph Laplacian regularization, it assumes that the spatial neighbors should have similar representation coefficients, the obtained coefficient matrix thus can reflect the similarity between samples more accurately. Besides, we also incorporate probabilistic class structure, which implies the probabilistic relationship between each sample and each class, into SR model to further improve discriminability of graph.The experimental results on Hyperion and AVIRIS hyperspectral data show that our method outperforms state of the art methods.