An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA

Ghimire, Bardan; Rogan, John; Galiano, Víctor Rodríguez; Panday, P. K.; Neeti, Neeti

doi:10.2747/1548-1603.49.5.623

Cited by 184 publications

(97 citation statements)

References 58 publications

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“…Furthermore, the utilization of non-parametric classification algorithms has proven to be an effective tool for mapping land cover using high number of input variables. Ensemble classification trees such as Random Forest are iterative and have been found to produce higher mapping accuracies and stable classification results compared to both parametric classifiers as well as non-iterative classification trees [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana

Mishra

Crews

Miller

et al. 2015

Land

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Savanna ecosystems are geographically extensive and both ecologically and economically important; they therefore require monitoring over large spatial extents. There are, in particular, large areas within southern Africa savanna ecosystems that lack consistent geospatial data on vegetation morphological properties, which is a prerequisite for biodiversity conservation and sustainable management of ecological resources. Given the challenges involved in distinguishing and mapping savanna vegetation assemblages using remote sensing, the objective of this study was to develop a vegetation morphology map for the largest protected area in Africa, the central Kalahari. Six vegetation morphology classes were developed and sample training/validation pixels were selected for each class by analyzing extensive in situ data on vegetation structural and functional properties, in combination with existing ancillary data and coarse scale land cover products. The classification feature set consisted of annual and intra annual matrices derived from 14 years of satellite-derived vegetation indices images, and final classification was achieved using an ensemble tree based classifier. All vegetation morphology classes were mapped with high accuracy and the overall classification accuracy was 91.9%. Besides filling the geospatial data gap for the central Kalahari area, this vegetation morphology map is expected to serve as a critical input to ecological studies focusing on habitat use by wildlife and the efficacy of game fencing, as well as contributing to sustainable ecosystem management in the central Kalahari. OPEN ACCESSLand 2015, 4 198

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Section: Introductionmentioning

confidence: 99%

Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana

Mishra

Crews

Miller

et al. 2015

Land

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“…In addition, random forest provides the relative importance of a variable using out-of-bag data when the variable is permuted. Because of these strengths, random forest has proven robust in various remote sensing applications [61][62][63][64][65][66][67][68].…”

Section: Machine Learning Algorithms For Lead Detectionmentioning

confidence: 99%

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Lee

Kim

et al. 2016

Remote Sensing

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Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.

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“…Popular MCS combination techniques include Bagging, Boosting, random forests, and AdaBoost with iterative and convergent nature [46,[52][53][54][55][56]. To obtain more base classifiers with differences, Ghimire and Rogan [54] performed land use/cover classification in a heterogeneous landscape in Massachusetts by comparing three combining techniques, i.e., bagging, boosting, and random, with decision tree algorithm, and their results showed that the MCS performed better than the decision tree classifier.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain more base classifiers with differences, Ghimire and Rogan [54] performed land use/cover classification in a heterogeneous landscape in Massachusetts by comparing three combining techniques, i.e., bagging, boosting, and random, with decision tree algorithm, and their results showed that the MCS performed better than the decision tree classifier. Based on SVM, Khosravi and Beigi [55] used bagging and AdaBoost to combine a MCS to classify a hyperspectral dataset, and their work has showed a high capability of MCS in classifying high dimensionality data.…”

Section: Introductionmentioning

confidence: 99%

Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique

2017

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Guangzhou has experienced a rapid urbanization since 1978 when China initiated the economic reform, resulting in significant land use/cover changes (LUC). To produce a time series of accurate LUC dataset that can be used to study urbanization and its impacts, Landsat imagery was used to map LUC changes in Guangzhou from 1987 to 2015 at a three-year interval using a multiple classifier system (MCS). The system was based on a weighted vector to combine base classifiers of different classification algorithms, and was improved using the AdaBoost technique. The new classification method used support vector machines (SVM), C4.5 decision tree, and neural networks (ANN) as the training algorithms of the base classifiers, and produced higher overall classification accuracy (88.12%) and Kappa coefficient (0.87) than each base classifier did. The results of the experiment showed that, based on the accuracy improvement of each class, the overall accuracy was improved effectively, which combined advantages from each base classifier. The new method is of high robustness and low risk of overfitting, and is reliable and accurate, and could be used for analyzing urbanization processes and its impacts.

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An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA

Cited by 184 publications

References 58 publications

Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana

Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique

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