Graphs naturally lend themselves to model the complexities of Hyperspectral Image (HSI) data as well as to serve as semi-supervised classifiers by propagating given labels among nearest neighbours. In this work, we present a novel framework for the classification of HSI data in light of a very limited amount of labelled data, inspired by multi-view graph learning and graph signal processing. Given an a priori superpixel-segmented hyperspectral image, we seek a robust and efficient graph construction and label propagation method to conduct semi-supervised learning (SSL). Since the graph is paramount to the success of the subsequent classification task, particularly in light of the intrinsic complexity of HSI data, we consider the problem of finding the optimal graph to model such data. Our contribution is two-fold: firstly, we propose a multi-stage edge-efficient semi-supervised graph learning framework for HSI data which exploits given label information through pseudo-label features embedded in the graph construction. Secondly, we examine and enhance the contribution of multiple superpixel features embedded in the graph on the basis of pseudo-labels in an extension of the previous framework, which is less reliant on excessive parameter tuning. Ultimately, we demonstrate the superiority of our approaches in comparison with state-of-the-art methods through extensive numerical experiments.
翻译:自然而然,图表可以模拟超光谱图像(HSI)数据的复杂性,也可以作为半监督的分类器,在近邻之间传播给定的标签。在这项工作中,我们提出了一个新的框架,用于根据非常有限的贴标签数据对高光光光谱数据进行分类,这种数据是由多视图图形学习和图形信号处理所启发的。鉴于一个先验的超像素组合超光谱图像,我们寻求一种强而高效的图形构建和标签传播方法,以进行半监督的学习(SSL)。由于该图对于随后的分类任务的成功至关重要,特别是鉴于HSI数据的内在复杂性,我们考虑了找到模拟这些数据的最佳图表的问题。我们的贡献有两个方面:首先,我们提议一个多阶段的边缘效率半监督的HSI数据学习框架,它利用通过嵌入图形的伪标签特征提供的标签信息。第二,我们研究并加强了在图形中嵌入的多个超级象素特征的贡献,其基础是假标签,其基础是模拟标签,其基础是模拟的模型,通过我们前一个参数的过度的比重化方法展示了我们最终的比重度。