This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.
@article{arxiv.1705.00727,
title = {Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network},
author = {Xiangyong Cao and Feng Zhou and Lin Xu and Deyu Meng and Zongben Xu and John Paisley},
journal= {arXiv preprint arXiv:1705.00727},
year = {2018}
}