Data Augmentation and Refining with Steering Stencils for Supervised Classification of Hyperspectral Image.

Limited and expensive availability of labeled training samples resulted in the development of methods defining the hyperspectral classification task in the form of data augmentation based supervised learning. However, most of the methods just implicitly utilize the spectral-spatial information in the isotropic neighborhood, instead of explicitly indicating the anisotropic or steering neighborhood system. In this paper, we apply steering stencils for estimating the local directional homogenous regions and exploiting more valuable spectral-spatial contexts. By using a best steering stencil matching method, we propose a data augmentation and refining method to improve the performance of any spectral-spatial classifier with limited labeled samples. Experiments show that the proposed method is very effective for many spectral-spatial classifiers.