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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Agricultural remote sensing big data: Management and applications
TL;DR: The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensingbig data management and applications at local regional and farm scale.
Journal ArticleDOI
Supervised Deep Feature Extraction for Hyperspectral Image Classification
TL;DR: The experimental results demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier can obtain better classification performance than that of the conventional methods.
Journal ArticleDOI
Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network
TL;DR: A novel dual-channel convolutional neural network framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI) and demonstrates that the DC-CNN-based method outperforms the state-of-the-art methods by a considerable margin.
Journal ArticleDOI
Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification
TL;DR: The proposed CDSAE framework comprises two stages with different optimization objectives, which can learn discriminative low-dimensional feature mappings and train an effective classifier progressively, and imposes a local Fisher discriminant regularization on each hidden layer of stacked autoencoder (SAE) to train discrim inative SAE (DSAE).
Journal ArticleDOI
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of hyperspectral images (HSIs).
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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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