Deep Learning-Based Classification of Hyperspectral Data
Citations
2,100 citations
Cites background or methods or result from "Deep Learning-Based Classification ..."
...CNN had also superior performance than Penalized Discriminant Analysis (Grinblat, Uzal, Larese, & Granitto, 2016), SVM Regression (Kuwata & Shibasaki, 2015), area-based techniques (Rahnemoonfar & Sheppard, 2017), texturebased regression models (Chen, et al., 2017), LMC classifiers (Xinshao & Cheng, 2015), Gaussian Mixture Models (Santoni, Sensuse, Arymurthy, & Fanany, 2015) and NaïveBayes classifiers (Yalcin, 2017 )....
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...%) and/or F1 scores (i.e. 0.558 - 0.746), however state of the art work in these particular problems has shown lower CA (i.e. SVM, RF, Naïve- Bayes classifier)....
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...The most popular techniques used for analyzing images include machine learning (ML) (K-means, support vector machines (SVM), artificial neural networks (ANN) amongst others), linear polarizations, wavelet-based filtering, vegetation indices (NDVI) and regression analysis (Saxena & Armstrong, 2014), (Singh, Ganapathysubramanian, Singh, & Sarkar, 2016)....
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...Some of the CNN approaches combined their model with a classifier at the output layer, such as logistic regression (Chen, Lin, Zhao, Wang, & Gu, 2014), Scalable Vector Machines (SVM) (Douarre, Schielein, Frindel, Gerth, & Rousseau, 2016), linear regression (Chen, et al., 2017), Large Margin Classifiers (LCM) (Xinshao & Cheng, 2015) and macroscopic cellular automata (Song, et al., 2016)....
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...Some of the CNN approaches combined their model with a classifier at the output layer, such as logistic regression (Chen et al., 2014), Scalable Vector Machines (SVM) (Douarre et al....
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2,095 citations
Cites background from "Deep Learning-Based Classification ..."
...SAE FOR HyPERSPECTRAL DATA CLASSIFICATION A first attempt in this direction can be found in [22], where the authors make use of an SAE to extract hierarchical features in the spectral domain....
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2,059 citations
Cites background from "Deep Learning-Based Classification ..."
..., stacked autoencoder (SAE), was proposed for HSI classification in 2014 [29]....
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1,625 citations
Cites background or methods from "Deep Learning-Based Classification ..."
...Straightforwardly, differing from the spectral–spatial classification scheme, the spectral and initial spatial features are combined together into a vector as the input of the DL network in a joint framework, as presented in the works [29], [53]–[55], [73], and [74]....
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...[74] adopted the stacked AE as the deep network structure....
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...In general, there are two main styles of classifiers: 1) the hard classifiers, such as SVMs, which directly output an integer number as the class label of each sample [76], and 2) the soft classifiers, such as logistic regression, which can simultaneously fine-tune the whole pretrained network and predict the class label in a probability distribution manner [29], [73], [74], [78]....
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1,316 citations
References
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15,055 citations
"Deep Learning-Based Classification ..." refers background in this paper
...Typical deep neural network architectures include deep belief networks (DBNs) [38], deep Boltzmann machines (DBMs) [39], SAEs [40], and stacked denoising AEs (SDAEs) [41]....
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11,201 citations
"Deep Learning-Based Classification ..." refers background in this paper
...sifiers like linear SVMand logistic regression can be attributed to single-layer classifiers, whereas decision tree or SVM with kernels are believed to have two layers [24]....
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...It is believed that deep architectures can potentially lead to progressively more abstract features at higher layers of feature, and more abstract features are generally invariant to most local changes of the input [24]....
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9,775 citations
"Deep Learning-Based Classification ..." refers methods in this paper
...The layer-wise training models have a bunch of alternatives such as restricted Boltzmann machines (RBMs) [42], pooling units [43], convolutional neural networks (CNNs) [44], AEs, and denoising AEs (DAE) [40]....
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