Hyperspectral Image Classification Based on Nonlinear Spectral-Spatial Network

Recently, for the task of hyperspectral image classification, deep-learning-based methods have revealed promising performance. However, the complex network structure and the time-consuming training process have restricted their applications. In this letter, we construct a much simpler network, i.e.,...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2016-12, Vol.13 (12), p.1782-1786
Main Authors: Pan, Bin, Shi, Zhenwei, Zhang, Ning, Xie, Shaobiao
Format: Article
Language:English
Subjects:
Classification
Deep learning
Feature extraction
hyperspectral image classification
Hyperspectral imaging
Image classification
Image processing
Imagery
Imaging
Kernel
Machine learning
Methods
nonlinear spectral–spatial network (NSSNet)
Principal component analysis
Principal components analysis
Spatial discrimination learning
Spectra
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Summary:Recently, for the task of hyperspectral image classification, deep-learning-based methods have revealed promising performance. However, the complex network structure and the time-consuming training process have restricted their applications. In this letter, we construct a much simpler network, i.e., the nonlinear spectral-spatial network (NSSNet), for hyperspectral image classification. NSSNet is developed from the basic structure of a principal component analysis network. Nonlinear information is included in NSSNet, to generate a more discriminative feature expression. Moreover, spectral and spatial features are combined to further improve the classification accuracy. Experimental results indicate that our method achieves better performance than state-of-the-art deep-learning-based methods.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2608963