Journal ArticleDOI
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network
Yushi Chen,Xing Zhao,Xiuping Jia +2 more
TLDR
A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN) and a novel deep architecture is proposed, which combines the spectral-spatial FE and classification together to get high classification accuracy.Abstract:
Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification.read more
Citations
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Journal ArticleDOI
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.
Journal ArticleDOI
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 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.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Journal ArticleDOI
Training products of experts by minimizing contrastive divergence
TL;DR: A product of experts (PoE) is an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary because it is hard even to approximate the derivatives of the renormalization term in the combination rule.