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Remote Sens. 2017, 9(1), 67; doi:10.3390/rs9010067

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

1
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China
2
Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 17 September 2016 / Revised: 5 January 2017 / Accepted: 9 January 2017 / Published: 13 January 2017
View Full-Text   |   Download PDF [10827 KB, uploaded 13 January 2017]   |  

Abstract

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record. View Full-Text
Keywords: hyperspectral image classification; deep learning; 2D convolutional neural networks; 3D convolutional neural networks; 3D structure hyperspectral image classification; deep learning; 2D convolutional neural networks; 3D convolutional neural networks; 3D structure
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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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Li, Y.; Zhang, H.; Shen, Q. Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens. 2017, 9, 67.

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