The application of artificial neural networks to the analysis of remotely sensed data

By Jean-François Mas, Juan J. Flores. 2008

550 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1cc9b7fe2a950358d6be85d96f4e0cdd2b8b9c9e

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Citations in the corpus

Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, background): “Additionally, studies which assess a new algorithm, comparing it with other methods, are likely to be biassed as a consequence of a better knowledge of the studied method (Mas and Flores, 2008).”
Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery (2020, methodology): “ANNs have successfully been applied to remote sensing in many fields([33,55-57]).”
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat (2010, background): “The total canopy chlorophyll content TC), which is the product of the two variables, shows a close relaion to the photosynthetic activity (McDonald, 2003) as well as to ∗ Corresponding author.”
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images (2017, methodology): “ANNs have successfully been applied to remote sensing in many fields (Mas & Flores, 2008; Zagajewski, 2010; Zhang & Xie, 2012).”
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification (2017, unknown): “In the context of spectral classification, decision trees [4], artificial neural networks [5], [6], and support vector”
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012, background): “Some textural variables, especially the bivariate measures, did not present a normal distribution (Mas and Flores, 2008).”
Global Research on Artificial Intelligence from 1990-2014: Spatially-Explicit Bibliometric Analysis (2016, background): “and much higher than other keywords throughout the study period [35–37].”
Prototype-Incorporated Emotional Neural Network (2018, background): “More important is that machines can boast of intelligence when such systems have the capability to learn (or adapt internal parameters) and upgrade its performance over time based on available experiential knowledge [9], [10]; this is analogous to learning”
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network (2017, background): “In recent years, various deep learning architectures such as Deep Belief Networks (DBN) [17], Convolutional Neural Networks (CNN) [18], and Recurrent Neural Networks (RNN) [19] have been applied to ﬁelds like computer vision [20,21], speech recognition, natural language processing, audio recognition”
How many hidden layers and nodes? (2009, methodology): “1990 ) there exists no exact method to answer this question (Mas and Flores 2008 ), and it is a critical question since the selection of topology has a profound impact on classification results.”
Inductive pattern-based land use/cover change models: A comparison of four software packages (2014, unknown): “…such as neural networks, genetic algorithms or SimWeight, are often perceived as black boxes that establish a transfer function between inputs (the explanatory variables) and output (change potential) without any knowledge of their internal working (Qiu and Jensen, 2004; Mas and Flores, 2008).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “A detailed overview of ANN approach is provided in Mas and Flores (2008).”