Hyperspectral Image Classification with Transfer Learning and Markov Random Fields

This letter provides a brand new way of feature extraction, which can be applied in the supervised classification of hyperspectral image. The convolutional neural network (CNN) has been proven to be an effective method of image classification. However, due to its long training time, it requires a large amount of the labeled data to achieve the expected outcome. To decrease the training time and reduce the dependence on large labeled data set, we propose using the method of transfer learning by taking the advantage of Bayesian framework to integrate with spectrum and spatial information, making use of the Markov property of images to distinguish and separate the ones with class tags, and employing the CNN trained by band samples randomly selected from the data sets. The method of classification mentioned in our letter makes use of the real hyperspectral data sets to perform the experimental evaluation. The result demonstrates that our method is superior to the previous methods.