Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

By Li Guo, Nesrine Chehata, Clément Mallet, Samia Boukir. 2011

457 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/78cce94d328225a4cc4e61783dcd365628df8df0

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Citations in the corpus

Analysis of full-waveform LiDAR data for classification of an orange orchard scene (2013, unknown): “To this list, Mallet et al. (2011) added backscattering coefficient, which was not tested in Guo et al. (2011), who in turn found two multispectral features (red and blue band) highly discriminative.”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, background): “Guo et al. [90] revealed in their classiﬁcation experiments that the relative height plays a signiﬁcant role in classiﬁcation accuracy and the best set of features should be a combination of both geometric and spectral information.”
Implementation of machine-learning classification in remote sensing: an applied review (2018, background): “Numerous studies have found this information useful, for example for evaluating the relative contribution of image and lidar variables (Guo et al. 2011; Maxwell et al. 2015) or the relative contribution of spectral, textural, and terrain variables (Hayes, Miller, and Murphy 2014) in classi ﬁ cation.”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, background): “RF – a variant of ensemble decision trees (Breiman, 2001) – have seen increased use in remote sensing for their ability to highlight variables considered important in a given classification (Guo et al., 2011; Pal, 2005).”
Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, methodology): “For example, Haala and Brenner (1999) perform pixel-based iterative self-organizing data analysis techniques clustering on both colour and infrared”
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012, methodology): “A Random Forest is an ensemble of classification trees, where each tree contributes with a single vote for the assignment of the most frequent class to the input data (Breiman, 2001; Guo et al., 2011; Rodriguez-Galiano et al., 2012b).”
Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, background): “2012) and requires fewer parameters without the assumptions of other nonparametric methods (Guo et al. 2011).”
Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, methodology): “It has been used, for example, to classify land cover [18–22] and ecological zones [23], to identify spectral differences of tree species [24–27], to map landslides [28], to create forest canopy fuel maps for fire forecasting [29], to quantify aboveground forest carbon pools [30], and to analyze urb”
Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, methodology): “RF is a regression technique that combines the performance of numerous DT algorithms to classify or predict the value of a variable (Breiman, 2001; Guo et al., 2011; Rodriguez-Galiano et al., 2012b).”