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Journal of Applied Remote Sensing

Spectral–spatial classification of hyperspectral image using three-dimensional convolution network
Author(s): Bing Liu; Xuchu Yu; Pengqiang Zhang; Xiong Tan; Ruirui Wang; Lu Zhi
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Paper Abstract

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

Paper Details

Date Published: 4 January 2018
PDF: 18 pages
J. Appl. Rem. Sens. 12(1) 016005 doi: 10.1117/1.JRS.12.016005
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Bing Liu, Zhengzhou Institute of Surveying and Mapping (China)
Xuchu Yu, Zhengzhou Institute of Surveying and Mapping (China)
Pengqiang Zhang, Zhengzhou Institute of Surveying and Mapping (China)
Xiong Tan, Zhengzhou Institute of Surveying and Mapping (China)
Ruirui Wang, Zhengzhou Institute of Surveying and Mapping (China)
Lu Zhi, Zhengzhou Institute of Surveying and Mapping (China)


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