Deep Learning-Based Classification of Hyperspectral Data

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 7; no. 6; pp. 2094 - 2107
Main Authors: Chen, Yushi, Lin, Zhouhan, Zhao, Xing, Wang, Gang, Gu, Yanfeng
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.06.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoencoder (AE)
Classification
deep learning
Feature extraction
hyperspectral data classification
Hyperspectral imaging
logistic regression
Logistics
Principal component analysis
Principal components analysis
Remote sensing
Satellites
Space surveillance
stacked autoencoder (SAE)
support vector machine (SVM)
Support vector machines
Training
deep learning
feature extraction
hyperspectral data classification
stacked autoencoder (SAE)
support vector machine (SVM)
Autoencoder (AE)
logistic regression
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate hyperspectral data classification.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2014.2329330