Random forest classification of multisource remote sensing and geographic data

Gislason, Pall Oskar; Benediktsson, Jón Atli; Sveinsson, Jóhannes R.

doi:10.1109/igarss.2004.1368591

Cited by 89 publications

(51 citation statements)

References 4 publications

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“…Classifiers have the same performance for classification of the joint dataset; SVM and RF have better performance compared to NN and MLC at 5% significance level. Parametric classification algorithms such as MLC are not typically suitable for multi source data [73,84]. The better performance of SVM and RF compared to NN could be because of the fact that both of these classifiers can handle high dimensional data [77,84].…”

Section: Discussionmentioning

confidence: 99%

“…Despite limitations due to its assumption of normal distribution of class signature [69], it is perhaps one of the most widely used classifiers [70][71][72]. Non-parametric approaches are suggested for the classification of multi-source data in complex environments [73]. SVM is a supervised non-parametric statistical learning technique [74] and it follows what is known as structural risk minimization.…”

Section: Determination Of the Land Cover Classification Schemementioning

confidence: 99%

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Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Attarchi

Gloaguen

2014

Remote Sensing

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Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning OPEN ACCESSRemote Sens. 2014, 6 3625 classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

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Section: Discussionmentioning

confidence: 99%

Section: Determination Of the Land Cover Classification Schemementioning

confidence: 99%

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Attarchi

Gloaguen

2014

Remote Sensing

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“…As illustrated previously, and Ntry are two sensitive parameters in the RF models. Ntry is the square root of the total number of variables [41]. influences the convergence of the RF and can be determined through the OOB error.…”

Section: Model Implementation and Validationmentioning

confidence: 99%

Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

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“…The assignment of class label of an unknown instance is performed using majority voting strategy. Due to the important advantages such as handling very large number of input attributes and low time cost, Random Forest has widely attracted the interests of researchers from the context of remote sensing image classification (Waske, B and Braun, M., 2009;Gislason et al, 2004;Qi et al, 2012;Samat et al, 2014).…”

Section: Training and Classification By Random Forestmentioning

confidence: 99%

Random Forest and Objected-Based Classification for Forest Pest Extraction From Uav Aerial Imagery

Hu¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: Superpixel, Simple Linear Iterative Cluster (SLIC), texture, Forest Pest, Random Forest, unmanned aerial vehicle (UAV) aerial imagery ABSTRACT:Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as soon as possible and by predicting the spreading ways of the infection. Now, with the UAV photography being more and more popular, it has become much cheaper and faster to get UAV images which are very suitable to be used to monitor the health of forest and detect the pest. This paper proposals a new method to effective detect forest pest in UAV aerial imagery. For an image, we segment it to many superpixels at first and then we calculate a 12-dimension statistical texture information for each superpixel which are used to train and classify the data. At last, we refine the classification results by some simple rules. The experiments show that the method is effective for the extraction of forest pest areas in UAV images.

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Random forest classification of multisource remote sensing and geographic data

Cited by 89 publications

References 4 publications

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Statistical Downscaling of Temperature with the Random Forest Model

Random Forest and Objected-Based Classification for Forest Pest Extraction From Uav Aerial Imagery

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