Dual Dictionary Learning for Mining a Unified Feature Subspace between Different Hyperspectral Image Scenes

In real-world applications of hyperspectral images (HSIs), we may frequently face the following situation: two HSI scenes (named source and target scenes, respectively) contain similar land over objects, but they are captured in different spots or at different time. Even if they are captured by the same hyper-spectral sensor, there exist spectral shift between them. In our previous work, we tried to solve the spectral shift by dictionary sharing or domain-invariant feature selection. However, a more regular case is that two similar HSI scenes are captured by different hyperspectral sensors. How to mine the relationship between such HSIs is a more challenging problem, since the feature spaces are totally different. A natural approach is to learn a unified low-dimensional feature subspace which can bridge the two HSI scenes. In this work, we propose a dual dictionary nonnegative matrix factorization (DDNMF) algorithm for the aforementioned goal. In details, an individual domain-specific dictionary is learned for each scene, and two dictionary learning tasks (for source and target scenes) are coupled by manifold regularization, ensuring that pixels belonging to a same land cover class have similar representations over the learned dictionaries, even if they come from different scenes. Experimental results show that the proposed algorithm can indeed mine a unified feature subspace shared between two different HSI scenes.