Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
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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Journal ArticleDOI
Deep learning in remote sensing applications: A meta-analysis and review
TL;DR: This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping, and a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.
Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.
Journal ArticleDOI
All-optical machine learning using diffractive deep neural networks
TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI
Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
TL;DR: An end-to-end spectral–spatial residual network that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification and achieves the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets.
Journal ArticleDOI
Implementation of machine-learning classification in remote sensing: an applied review
TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).
References
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Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
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