NDLI: Spatial-spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

Content Provider	IET Digital Library
Author	Qing, Chunmei Ruan, Jiawei Xu, Xiangmin Ren, Jinchang Zabalza, Jaime
Abstract	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.
Starting Page	235
Ending Page	245
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5727
Journal	IET Image Processing
Publisher Date	2018-10-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Belief Network Class Posterior Probability Distribution CNN Model Computer Vision And Image Processing Technique Convolutional Neural Network Data Classification Deep Learning Framework Deep Spectral Feature Geography And Cartography Computing Geophysical Image Processing Geophysical Technique And Equipment HSI Hyperspectral Image Image Classification Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Knowledge Engineering Technique Learning in AI Marginal Probability Distribution Markov Processes Markov Random Field MRF-based Loopy Belief Propagation Neural Computing Technique Neural Nets Probability Probability Theory Regression Analysis Remote Sensing Spatial Feature Spatial Information Spatial-spectral Classification Spectral Feature Statistical Distribution Statistics Stochastic Linearised SCUC
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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