1 research outputs found
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing
Recent advances in neural networks have made great progress in the
hyperspectral image (HSI) classification. However, the overfitting effect,
which is mainly caused by complicated model structure and small training set,
remains a major concern. Reducing the complexity of the neural networks could
prevent overfitting to some extent, but also declines the networks' ability to
express more abstract features. Enlarging the training set is also difficult,
for the high expense of acquisition and manual labeling. In this paper, we
propose an abundance-based multi-HSI classification method. Firstly, we convert
every HSI from the spectral domain to the abundance domain by a
dataset-specific autoencoder. Secondly, the abundance representations from
multiple HSIs are collected to form an enlarged dataset. Lastly, we train an
abundance-based classifier and employ the classifier to predict over all the
involved HSI datasets. Different from the spectra that are usually highly
mixed, the abundance features are more representative in reduced dimension with
less noise. This benefits the proposed method to employ simple classifiers and
enlarged training data, and to expect less overfitting issues. The
effectiveness of the proposed method is verified by the ablation study and the
comparative experiments