In this article, we have great interest in investigating a common but practical issue in remote sensing (RS)--can a limited amount of one information-rich (or high-quality) data, e.g., hyperspectral (HS) image, improve the performance of a classification task using a large amount of another information-poor (low-quality) data, e.g., multispectral (MS) image? This question leads to a typical cross-modality feature learning. However, classic cross-modality representation learning approaches, e.g., manifold alignment, remain limited in effectively and efficiently handling such problems that the data from high-quality modality are largely absent. For this reason, we propose a novel graph-induced aligned learning (GiAL) framework by 1) adaptively learning a unified graph (further yielding a Laplacian matrix) from the data in order to align multimodality data (MS-HS data) into a latent shared subspace; 2) simultaneously modeling two regression behaviors with respect to labels and pseudo-labels under a multitask learning paradigm; and 3) dramatically updating the pseudo-labels according to the learned graph and refeeding the latest pseudo-labels into model learning of the next round. In addition, an optimization framework based on the alternating direction method of multipliers (ADMMs) is devised to solve the proposed GiAL model. Extensive experiments are conducted on two MS-HS RS data sets, demonstrating the superiority of the proposed GiAL compared with several state-of-the-art methods.

中文翻译：

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高光谱和多光谱数据子空间的图诱导对齐学习

在本文中，我们非常有兴趣研究遥感 (RS) 中一个常见但实际的问题——有限数量的信息丰富（或高质量）数据，例如高光谱 (HS) 图像，可以改善使用大量其他信息差（低质量）数据（例如多光谱 (MS) 图像）的分类任务的性能？这个问题导致了典型的跨模态特征学习。然而，经典的跨模态表示学习方法，例如流形对齐，在有效和高效地处理此类问题方面仍然受到限制，从而导致高质量模态的数据在很大程度上缺失。为此原因，我们提出了一种新颖的图诱导对齐学习 (GiAL) 框架：1) 从数据中自适应地学习统一图（进一步产生拉普拉斯矩阵），以便将多模态数据（MS-HS 数据）对齐到潜在的共享子空间中；2）在多任务学习范式下同时对标签和伪标签的两种回归行为进行建模；3）根据学习到的图大幅更新伪标签，并将最新的伪标签重新反馈到下一轮的模型学习中。此外，设计了一个基于乘法器交替方向法 (ADMM) 的优化框架来解决所提出的 GiAL 模型。在两个 MS-HS RS 数据集上进行了大量实验，证明了所提出的 GiAL 与几种最先进的方法相比的优越性。