A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
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Cites background or methods from "A Comprehensive Survey of Deep Lear..."
...Contextualizing the evolution of DL methods, at early days, neural models emerged within the fields of pattern recognition and signal processing, inspired by the behaviour of the biological brain and implementing a hierarchical structure where each part of the stack conforms a layer, being neurons (also perceptrons) the basic unit of each layer (Ball et al., 2017)....
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...DBNs combine probability and graph theory to implement a generative probabilistic graphical model (PGM) with the structure of a directed acyclic graph (DAG) (Ball et al., 2017)....
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...These coupled networks are trained together as an end-to-end model to optimize all the weights in the CNN: (i) the FE-net, composed by a hierarchical stack of feature extraction and detection stages that learns high-level representations of the inputs, and (ii) the classifier, composed by a stack of FC layers that performs the final classification task, computing the membership of each input sample to a certain class (Ball et al., 2017)....
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...erative probabilistic graphical model (PGM) with the structure of a directed acyclic graph (DAG) (Ball et al., 2017)....
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