A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forest...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 11; no. 13; p. 1600
Main Authors: Camargo, Flávio F., Sano, Edson E., Almeida, Cláudia M., Mura, José C., Almeida, Tati
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2019
Subjects:
Agricultural land
Artificial intelligence
Biomass
Cerrado
Classification
Continuity (mathematics)
Data mining
Decomposition
Domains
Ecosystems
Grasslands
Highways
Hyperplanes
Image classification
Land cover
Land use
Learning algorithms
Machine learning
Neural networks
Peptides
Polarimetry
Reforestation
Remote sensing
SAR
Satellites
Studies
thematic mapping
Upper bounds
Urban areas
Vegetation
Brazil
Cerrado Biome
New York
South Africa
United Kingdom > UK
United States > US
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Summary:This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11131600