Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Sheykhmousa, Mohammadreza; Mahdianpari, Masoud; Ghanbari, Hamid; Mohammadimanesh, Fariba; Ghamisi, Pedram; Homayouni, Saeid

doi:10.1109/jstars.2020.3026724

Cited by 642 publications

(325 citation statements)

References 144 publications

(147 reference statements)

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“…There are many supervised classification algorithms, such as the maximum likelihood, single linkage, Mahalanobis distance, support vector machines and random forests. Among them, support vector machines (SVMs) and random forests (RFs) have been widely used in recent years, as they typically offer superior classification [ 10 , 11 , 12 , 13 ]. Compared with traditional statistical theory, SVMs with simple structures and strong generalization ability can solve a large number of small-sample learning problems [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Han

Huang

et al. 2021

Sensors

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This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

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Section: Introductionmentioning

confidence: 99%

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Han

Huang

et al. 2021

Sensors

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“…The developed approach consists of two main steps: (i) slum area extraction from high-resolution satellite images using We used the Support Vector Machine (SVM) method as the main classifier to extract the deprived/slum areas from the high-resolution satellite images. SVM is a kernel-based non-parametric supervised machine learning algorithm, which is widely used for image classification tasks and produces reliable results [55][56][57]. SVM splits and groups the data by identifying a single linear boundary in its basic form.…”

Section: Methodsmentioning

confidence: 99%

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Ghaffarian

Emtehani

2021

Climate

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Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

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“…The random forest method is a powerful classification scheme proposed by Breiman [56], which has been successfully used in various LULC classification projects [53,[57][58][59][60][61]. As pointed out by Sheykhmousa et al [57], the main advantage of RF is that it can provide very high classification results, and in addition this algorithm is robust to outliers and works well in noisy environments [53]. The working mechanism for RF can be summed up as follows: first, using the LULC training samples, this algorithm separates the training samples into subsets with the use of the bagging technique [62].…”

Section: Landsat Image Classification and Accuracy Assessmentmentioning

confidence: 99%

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

Bình¹,

Nhut²,

An³

et al. 2021

IJGI

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The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

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Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Cited by 642 publications

References 144 publications

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

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