Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
This work addresses the problem of limited training data in hyperspectral image classification, offering an incremental improvement with a novel discriminative penalty and regularization.
The paper tackled the suboptimal greedy training in deep dictionary learning for hyperspectral image classification by proposing a joint learning approach with a new discriminative penalty and stochastic regularization, resulting in state-of-the-art performance over existing methods.
In recent studies in hyperspectral imaging, biometrics and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data is limited; therefore hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence sub-optimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.