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 Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples
TL;DR: The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples, and time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.
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Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network
TL;DR: A global–local hierarchical weighted fusion end-to-end classification architecture for spectral–spatial fusion based on deep learning that is more competitive in terms of accuracy and generalization is proposed.
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Automatic mapping of urban green spaces using a geospatial neural network
TL;DR: In this paper, a detailed and precise urban green spaces (UGS) maps provide essential data for sustainable urban development and related studies (e.g. heatwave events, heat related health risk, urban flooding,...
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A Batch-Mode Regularized Multimetric Active Learning Framework for Classification of Hyperspectral Images
Zhou Zhang,Melba M. Crawford +1 more
TL;DR: A regularized multimetric active learning (AL) framework is proposed which consists of three main parts, in which a regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small.
Journal ArticleDOI
Semisupervised graph convolutional network for hyperspectral image classification
TL;DR: This work explores the small sample classification problem of HSI with graph convolutional network (GCN) with the result that the proposed method outperforms the traditional semisupervised methods and advanced deep learning methods.
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