Bayesian probability

Image segmentation

Pattern recognition (psychology)

Mathematics

Contextual image classification

Image (mathematics)

Statistics

Geodesy

Hyperspectral imaging

Pixel

Benchmark (surveying)

Artificial intelligence

Spatial analysis

Geography

Markov random field

Computer science

Convolutional neural network

IEEE Transactions on Image Processing

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 and update the class labels of all pixel vectors using -expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network