Hapke Data Augmentation for Deep Learning-Based Hyperspectral Data Analysis With Limited Samples

The emerging technology of deep neural networks has been proven to be successful for hyperspectral image analysis. However, it is still a great challenge to apply the deep learning method for quantitatively retrieving mineralogical composition, because typical deep neural networks generally require thousands of labeled samples for training, while only a few mineral samples can be acquired and examined for quantitative examination in practice. To address this challenge, this letter proposes a training data augmentation approach which incorporates the prior-knowledge of hyperspectral reflectance characteristics using the classic Hapke equations. Experiments over both laboratory and airborne hyperspectral remote sensing data show that the proposed method outperforms the widely used approaches for quantitative mineral analysis.