Random forest classifier for remote sensing classification

Pal, Mahesh

doi:10.1080/01431160412331269698

Cited by 2,630 publications

(1,228 citation statements)

References 6 publications

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“…This is consistent with the results of other studies (Duro et al., 2012; Pal, 2005; Roli & Fumera, 2001), where it was found that RF and SVM were more successful, especially under complex conditions. One can argue that, in this study, ecological redundancy contributed to complexity and that it offers an explanation why these classifiers performed better than NN and DT.…”

Section: Discussionmentioning

confidence: 99%

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

MacIntyre

Niekerk

Dobrowolski

et al. 2018

Ecology and Evolution

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Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.

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Section: Discussionmentioning

confidence: 99%

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

MacIntyre

Niekerk

Dobrowolski

et al. 2018

Ecology and Evolution

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“…RF, showing performance at the level of boosting and support vector machines, is one of the most successful ensemble methods and an effective tool in prediction. Recently, both of them have been successfully applied in many fields including ecology, bio-informatics, genetics and earth science (remote sensing) (Moisen and Frescino, 2002;Chen and Liu, 2005;Dolan and Parker, 2005;Pal, 2005;Barker et al, 2006;Cutler et al, 2007;De'ath, 2007;Peters et al, 2007;Elith et al, 2008;Perdiguero-Alonso et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Predicting assemblages and species richness of endemic fish in the upper Yangtze River

Wang

Lek-Ang

et al. 2010

Science of The Total Environment

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“…As a result, different classification results are obtained from each tree, and a simple majority vote is used to create the final classification result. The RF technique has been applied to a wide variety of disciplines, and in the last decade it has been used with success in remote sensing applications including SAR classification studies [14,37,38].…”

Section: Classification Algorithmmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

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Random forest classifier for remote sensing classification

Cited by 2,630 publications

References 6 publications

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

Predicting assemblages and species richness of endemic fish in the upper Yangtze River

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

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