%0 Electronic Article
%A Chunmei Qing
%A Jiawei Ruan
%A Xiangmin Xu
%A Jinchang Ren
%A Jaime Zabalza
%K convolutional neural networks
%K class posterior probability distribution
%K data classification
%K hyperspectral images
%K CNN model
%K spatial information
%K spectral features
%K spatial features
%K HSI
%K MRF-based loopy belief propagation
%K deep spectral feature
%K spatial-spectral classification
%K marginal probability distribution
%K Markov random fields
%K deep learning framework
%X For the spatial-spectral classification of hyperspectral images (HSIs), a deep learning framework is proposed in this study, which consists of convolutional neural networks (CNNs) and Markov random fields (MRFs). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilised as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic prior for regularising the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation. In comparison with several state-of-the-art approaches for data classification on three publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.
%@ 1751-9659
%T Spatial-spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling
%B IET Image Processing
%D February 2019
%V 13
%N 2
%P 235-245
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=3m60wm6wn7ega.x-iet-live-01content/journals/10.1049/iet-ipr.2018.5727
%G EN