scholarly journals Comparison of Accuracy Between Support Vector Machine and Random Forest Classifiers for Land Use and Crop Mapping Using Multi-Temporal Sentinel-2 Images

2021 ◽  
Vol 12 (4) ◽  
pp. 73-92
Author(s):  
Zeinab Ghodsi ◽  
Mir Masoud Kheirkhah Zarkesh ◽  
Bagher Ghermezcheshmeh
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Support Vector ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Sentinel 2

scholarly journals LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1841-1847 ◽  
Author(s):  
L. E. Christovam ◽  
G. G. Pessoa ◽  
M. H. Shimabukuro ◽  
M. L. B. T. Galo
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Principal Components ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Spectral Angle Mapper ◽  
Spectral Angle

<p><strong>Abstract.</strong> Land Use and Land Cover (LULC) information is an important data source for modeling environmental variables, so it is essential to develop high quality LULC maps. The hundreds of continuous spectral bands gathered with hyperspectral sensors provide high spectral detail and consequently confirm hyperspectral remote sensing as an appropriate option for many LULC applications. Despite increased spectral detail, issues like high dimensionality, huge volume of data and redundant information, mean that hyperspectral image classification is a complex task. It is therefore essential to develop classification approaches that deals with these issues. Since classification results are directly dependent on the dataset used, it is fundamental to compare and validate the classification approaches in public datasets. With this in mind, aiming to provide a baseline, four classification models in the relatively new hyperspectral HyRANK dataset were evaluated. The classification models were defined with three well-known classification algorithms: Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Random Forest (RF). A classification model with SAM and another with RF were defined with the 176 surface reflectance bands. A dimensionality reduction with principal component analysis was carried out and a classification model with SVM and another with RF were defined using 14 principal components as features. The results show that SVM and RF algorithms outperformed by far the SAM in terms of accuracy, and that the RF is slightly better than the SVM in this respect. It is also possible to see from the results that the use of principal components as features provided an improvement in the accuracy of the RF and an improvement of 28% in the time spent fitting the classification model.</p>

scholarly journals SENTINEL-1 IMAGE CLASSIFICATION FOR CITY EXTRACTION BASED ON THE SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 297-302 ◽  
Author(s):  
A. Jamali ◽  
A. Abdul Rahman
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Image Classification ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
The Impact

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.

scholarly journals A FIT-FOR-PURPOSE ALGORITHM FOR ENVIRONMENTAL MONITORING BASED ON MAXIMUM LIKELIHOOD, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 25-32 ◽  
Author(s):  
A. Jamali
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Land Cover ◽  
Environmental Monitoring ◽  
Support Vector ◽  
Earth Surface ◽  
Land Use Land Cover ◽  
Fit For Purpose

<p><strong>Abstract.</strong> Due to concerns of recent earth climate changes such as an increase of earth surface temperature and monitoring its effect on earth surface, environmental monitoring is a necessity. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modelling as a key factor to investigate impact of climate change phenomena such as droughts and floods on earth surface land cover. There are several free and commercial multi/hyper spectral data sources of Earth Observation (EO) satellites including Landsat, Sentinel and Spot. In this paper, for land use land cover modelling (LULCM), image classification of Landsat 8 using several mathematical and machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood (ML) and a combination of SVM, ML and RF as a fit-for-purpose algorithm are implemented in R programming language and compared in terms of overall accuracy for image classification.</p>

scholarly journals Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)

2021 ◽  
Vol 13 (19) ◽  
pp. 3899
Author(s):  
Guanyao Xie ◽  
Simona Niculescu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Coastal Area ◽  
Machine Learning Algorithms ◽  
High Rate ◽  
Support Vector

Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula.

scholarly journals Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States

2020 ◽  
Vol 12 (9) ◽  
pp. 3544 ◽  
Author(s):  
Tobias Jensen ◽  
Frederik Seerup Hass ◽  
Mohammad Seam Akbar ◽  
Philip Holm Petersen ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Invasive Species ◽  
Support Vector Machine ◽  
Random Forest ◽  
The United States ◽  
Support Vector ◽  
Area Of Interest ◽  
Responsible Consumption ◽  
Sentinel 2

Invasive plants are causing massive economic and environmental troubles for our societies worldwide. The aim of this study is to employ a set of machine learning classifiers for detecting invasive plant species using remote sensing data. The target species is Kudzu vine, which mostly grows in the south-eastern states of the US and quickly outcompetes other plants, making it a relevant and threatening species to consider. Our study area is Atlanta, Georgia and the surrounding area. Five different algorithms: Boosted Logistic Regression (BLR), Naive Bayes (NB), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM) were tested with the aim of testing their performance and identifying the most optimal one. Furthermore, the influence of temporal, spectral and spatial resolution in detecting Kudzu was also tested and reviewed. Our finding shows that random forest, neural network and support vector machine classifiers outperformed. While the achieved internal accuracies were about 97%, an external validation conducted over an expanded area of interest resulted in 79.5% accuracy. Furthermore, the study indicates that high accuracy classification can be achieved using multispectral Sentinel-2 imagery and can be improved while integrating with airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral data. Finally, this study indicates that dimensionality reduction methods such as principal component analysis (PCA) should be applied cautiously to the hyperspectral AVIRIS data to preserve its utility. The applied approach and the utilized set of methods can be of interest for detecting other kinds of invasive species as part of fulfilling UN sustainable development goals, particularly number 12: responsible consumption and production, 13: climate action, and 15: life on land.

scholarly journals Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam

2020 ◽  
Vol 12 (9) ◽  
pp. 1367 ◽  
Author(s):  
Huong Thi Thanh Nguyen ◽  
Trung Minh Doan ◽  
Erkki Tomppo ◽  
Ronald E. McRoberts
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Confusion Matrix ◽  
Support Vector ◽  
Remotely Sensed Data ◽  
K Nearest Neighbors ◽  
Tropical Regions ◽  
Multi Temporal ◽  
Sentinel 2

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km2 study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km2 (1% of the study area) to 2200 km2 (34% of the study area) with greater uncertainties for smaller classes.

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