Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling

Qing, Chunmei and Ruan, Jiawei and Xu, Xiangmin and Ren, Jinchang and Zabalza, Jaime (2019) Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling. IET Image Processing, 13 (2). pp. 235-245. ISSN 1751-9659 (https://doi.org/10.1049/iet-ipr.2018.5727)

[thumbnail of Qing-etal-IETIP2018-Spatial-spectral-classification-of-hyperspectral-images-a-deep-learning]
Preview
 Text. Filename: Qing_etal_IETIP2018_Spatial_spectral_classification_of_hyperspectral_images_a_deep_learning.pdf
Accepted Author Manuscript
Download (2MB)| Preview

Abstract

For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). 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 utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing 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 (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.

ORCID iDs

Qing, Chunmei, Ruan, Jiawei, Xu, Xiangmin, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194 and Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725;
CORE (COnnecting REpositories)