Deep Learning-Based Classification of Hyperspectral Data
Top Cited Papers
- 26 June 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Vol. 7 (6), 2094-2107
- https://doi.org/10.1109/jstars.2014.2329330
Abstract
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.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities (2013028)
- National Natural Science Foundation of China (61301206, 61371180)
This publication has 38 references indexed in Scilit:
- Hyperspectral Remote Sensing Data Analysis and Future ChallengesIEEE Geoscience and Remote Sensing Magazine, 2013
- Spatial-Spectral Kernel Sparse Representation for Hyperspectral Image ClassificationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013
- Spectral–Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active LearningIEEE Transactions on Geoscience and Remote Sensing, 2012
- Deep Belief Networks Are Compact Universal ApproximatorsNeural Computation, 2010
- Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering TechniquesIEEE Transactions on Geoscience and Remote Sensing, 2009
- Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological ProfilesIEEE Transactions on Geoscience and Remote Sensing, 2008
- Deep, Narrow Sigmoid Belief Networks Are Universal ApproximatorsNeural Computation, 2008
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Theory of the backpropagation neural networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989