A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

He, Jinjie; Harris, Jeff; Sawada, M.; Behnia, Pouran

doi:10.1080/01431161.2015.1035410

Cited by 67 publications

(36 citation statements)

References 36 publications

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“…Sluiter and Pebesma [11] compared seven classification techniques in Mediterranean heterogeneous landscapes, concluding that the more accurate results were obtained both with RF and SVM. Similar results were reported by He et al [12] when mapping Arctic lithology in Canada. Rodríguez-Galiano [13] and Rodriguez-Galiano et al [14] used several different methods to classify land use in a semiarid environment in southern Spain.…”

Section: Introductionsupporting

confidence: 80%

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Castillo¹,

Alonso‐Sarría²,

Cánovas-García³

2017

Preprint

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Abstract:The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

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Section: Introductionsupporting

confidence: 80%

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Castillo¹,

Alonso‐Sarría²,

Cánovas-García³

2017

Preprint

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“…Multiple CART-like trees are created by Random Forest in training step (Breiman et al, 1994). To determine a split for each node, bootstrapped technique is used and randomly selected subsets from input variables are searched (He et al, 2015;Gislason et al, 2006). CART algorithm uses GINI index to determine the best split (Gislason et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Bayram

Erdem

Akpınar

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled" Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model -Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example ". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and waterbody classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5 th band) and GOKTURK-2 (4 th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.

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“…Recent literature, however, tend to focus on the latter, especially SVM and RF [Chabrier et al, 2012;Naidoo et al 2014;Sonobe et al, 2014;Clewley et al 2015]. Both approaches have known being consistently superior than conventional methods such as maximum likelihood classification or decision trees [Rodriguez-Galiano and Chica-Rivas, 2014;He et al, 2015;Low et al 2015].…”

Section: Introductionmentioning

confidence: 99%

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

Trisasongko

2017

European Journal of Remote Sensing

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This paper presents an evaluation on strategies for rubber plantation mapping employing SAR data coupled with Random Forest (RF) and Support Vector Machine (SVM). Linear backscatter coefficients achieved saturation point at about 10 years, making this form of polarimetric data being robust only for young to mature stands. This research found that the performance of both algorithms was comparable. The addition of texture features gave substantial impact to the overall accuracy. As indicated by the analysis of variable importance, only some texture features have contributed to higher overall accuracy. Classification using a subset of texture features pointed out that accuracy could be improved using dual polarimetric data, while trivial enhancement was seen in combined HH, HV and VV backscatter intensities. The research showed that classification accuracy could be further augmented by setting proper classification parameters. Nonetheless, it is argued that the level of improvement would greatly depend on selecting a proper dataset fed into classifier, rather than tuning classifier parameters. ARTICLE HISTORY

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A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

Cited by 67 publications

References 36 publications

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

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A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

Cited by 67 publications

References 36 publications

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000&ndash;2015 in a Mediterranean Semiarid Area

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000&ndash;2015 in a Mediterranean Semiarid Area

The Efficiency of Random Forest Method for Shoreline Extraction From Landsat-8 and Gokturk-2 Imageries

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

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Product

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Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area

Using Different Algorithms and Multi-Seasonal, Textural and Ancillary Information to Increase Classification Accuracy during the Period 2000–2015 in a Mediterranean Semiarid Area