Hyperspectral Image Segmentation with Markov Random Fields and a Convolutional Neural Network

This paper presents a new supervised segmentation algorithm for hyperspectral image (HSI) data which integrates both spectral and spatial information in a probabilistic framework. A convolutional neural network (CNN) is first used to learn the posterior class distributions using a patch-wise training strategy to better utilize the spatial information. Then, the spatial information is further considered by using a Markov random field prior, which encourages the neighboring pixels to have the same labels. Finally, a maximum a posteriori segmentation model is efficiently computed by the alpha-expansion min-cut-based optimization algorithm. The proposed segmentation approach achieves state-of-the-art performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.