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Citations in the corpus

Generalized Composite Kernel Framework for Hyperspectral Image Classification (2013, methodology): “parameter search between minima and maxima values according to our experiments with the same hyperspectral scenes in previous contributions [11], [14], [33] and selected the optimal”
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, unknown): “In [26], a new classification framework was proposed to exploit the spatial and spectral information using loopy belief propagation and active learning.”
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, background): “It has received a lot of attention in the remote sensing literature [50]–[52].”
Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017, methodology): “In addition, some extensions of the SVM model in hyperspectral image classification have been presented to improve the classification performance [21], [22].”
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, background): “Digital Object Identifier 10.1109/TGRS.2013.2264508 images [6], e.g., random forests [7], [8], neural networks [9], [10], AdaBoost [11], support vector machines (SVMs) [12], sparse representation [13], [14], and active learning [15]–[17] methods.”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, methodology): “In [21], the proposed framework extracted the spatial and spectral information using loopy belief propagation and”
Deep Learning-Based Classification of Hyperspectral Data (2014, background): “In [33], a new classification framework is proposed to exploit the spatial and spectral information using loopy belief propagation and active learning.”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, methodology): “Reference [95] introduced loopy belief propagation (LBP) strategy to infer MAPM, which directly determined the output of spectral– spatial classification.”