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Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
School of Information Engineering, Jiangxi University of Science and Technology, GanZhou 341000, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1896;
Received: 13 July 2019 / Revised: 12 August 2019 / Accepted: 12 August 2019 / Published: 13 August 2019
(This article belongs to the Section Remote Sensing Image Processing)
PDF [2896 KB, uploaded 16 August 2019]


Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like an ensemble of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gradient flow and replaces the stacking of multiple residual blocks in ResNet with fewer residual blocks but more parallel residual functions in each of it. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art classification methods. View Full-Text
Keywords: hyperspectral image (HSI) classification; convolutional neural network (CNN); deep learning; residual network (ResNet); ensemble hyperspectral image (HSI) classification; convolutional neural network (CNN); deep learning; residual network (ResNet); ensemble

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Meng, Z.; Li, L.; Tang, X.; Feng, Z.; Jiao, L.; Liang, M. Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification. Remote Sens. 2019, 11, 1896.

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