Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
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TLDR
The concept of deep learning is introduced into hyperspectral data classification for the first time, and a new way of classifying with spatial-dominated information is proposed, which is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression.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.read more
Citations
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Deep learning in agriculture: A survey
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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.
Journal ArticleDOI
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Liangpei Zhang,Lefei Zhang,Bo Du +2 more
TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
Journal ArticleDOI
Deep Convolutional Neural Networks for Hyperspectral Image Classification
TL;DR: Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
References
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Proceedings ArticleDOI
Support vector machines for classification of hyperspectral data
J.A. Gualtieri,S. Chettri +1 more
TL;DR: The support vector machine, recently introduced by Boser, Guyon, and Vapnik is useful in solving supervised classification in high dimensions and its application to high dimensional hyperspectral data taken from NASA's AVIRIS sensor and from a commercially available sensor called AISA.
Proceedings ArticleDOI
Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia
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