A novel statistical metric learning for hyperspectral image classification
This work addresses spectral-spatial classification in hyperspectral imaging, which is important for remote sensing applications, but appears to be an incremental improvement over existing metric learning techniques.
The authors tackled hyperspectral image classification by developing a statistical metric learning method that reduces intra-class variance and increases inter-class separation. Their approach achieved effective results on two real-world datasets, though no specific numerical improvements were reported.
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image. First, the standard variance of the samples of each class in each batch is used to decrease the intra-class variance within each class. Then, the distances between the means of different classes are used to penalize the inter-class variance of the training samples. Finally, the standard variance between the means of different classes is added as an additional diversity term to repulse different classes from each other. Experiments have conducted over two real-world hyperspectral image datasets and the experimental results have shown the effectiveness of the proposed statistical metric learning.