Label Propagation Ensemble for Hyperspectral Image Classification
Label Propagation Ensemble for Hyperspectral Image Classification
复制标题
用于高光谱图像分类的标签传播集成
DOI：
10.1109/jstars.2019.2926123
复制
发表时间：
2019-09-01
影响因子：
5.5
通讯作者：
Fu, Peng
中科院分区：
文献类型：
--
作者：
Zhang, Youqiang;Cao, Guo;Fu, Peng
有限的标记像素与高光谱数据的高维数之间的不平衡很容易导致Hughes现象。半监督学习（SSL）方法为解决上述问题提供了有前途的解决方案。基于图的SSL算法，也被称为标签传播方法，在高光谱图像（HSI）分类中得到了越来越多的关注。然而，通过利用样本的几何结构相似性构造的图是不可靠的，由于高维度和复杂性的HSI，特别是对于非常有限的标记像素的情况下。我们的动机是建立标签传播集成（LPE）模型，然后使用多个标签传播的决策融合，以获得高分类置信度的伪标记像素。LPE算法引入随机子空间方法将特征空间划分为多个子空间，然后在相应的子空间上构造多个标签传播模型，最后在决策层融合不同标签传播模型的结果，只有标签传播结果相同的未标记像素才被分配伪标签。同时，在迭代过程中，极端学习机器分类器在标记和伪标记样本上进行训练。与传统的标签传播方法相比，该方法能够提供具有高分类置信度的伪标签像素，从而能够处理标签样本非常有限的情况，获得准确的基分类器。为了证明所提出的方法的有效性，LPE与几个国家的最先进的方法在四个高光谱数据集进行了比较。此外，本文还研究了仅使用标签传播的方法，以说明集成技术在LPE中的重要性。实验结果表明，该方法可以为HSI分类提供有竞争力的解决方案。
The imbalance between limited labeled pixels and high dimensionality of hyperspectral data can easily give rise to Hughes phenomenon. Semisupervised learning (SSL) methods provide promising solutions to address the aforementioned issue. Graph-based SSL algorithms, also called label propagation methods, have obtained increasing attention in hyperspectral image (HSI) classification. However, the graphs constructed by utilizing the geometrical structure similarity of samples are unreliable due to the high dimensionality and complexity of the HSIs, especially for the case of very limited labeled pixels. Our motivation is to construct label propagation ensemble (LPE) model, then use the decision fusion of multiple label propagations to obtain pseudolabeled pixels with high classification confidence. In LPE, random subspace method is introduced to partition the feature space into multiple subspaces, then several label propagation models are constructed on corresponding subspaces, finally the results of different label propagation models are fused at decision level, and only the unlabeled pixels whose label propagation results are the same will be assigned with pseudolabels. Meanwhile extreme learning machine classifiers are trained on the labeled and pseudolabeled samples during the iteration. Compared with traditional label propagation methods, our proposed method can deal with the situation of very limited labeled samples by providing pseudolabeled pixels with high classification confidence, consequently, the accurate base classifiers are obtained. To demonstrate the effectiveness of the proposed method, LPE is compared with several state-of-the-art methods on four hyperspectral datasets. In addition, the method that only use label propagation is investigated to show the importance of ensemble technique in LPE. The experimental results demonstrate that the proposed method can provide competitive solution for HSI classification.