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Automatic cost-effective method for land cover classification (ALCC) (CROSBI ID 261479)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Gašparović, Mateo ; Zrinjski, Mladen ; Gudelj, Marina Automatic cost-effective method for land cover classification (ALCC) // Computers environment and urban systems, 76 (2019), 4; 1-10. doi: 10.1016/j.compenvurbsys.2019.03.001

Podaci o odgovornosti

Gašparović, Mateo ; Zrinjski, Mladen ; Gudelj, Marina

engleski

Automatic cost-effective method for land cover classification (ALCC)

The need for the detection and monitoring of changes in the environment is greater today than ever before. Through classification we can obtain insights into the state of the land surface. No known classification methods are fully automated, and their implementation requires preprocessing and postprocessing. This research provides a novel, fully automatic and cost-effective land cover classification method (ALCC). This novel automatic method does not require prior knowledge of the terrain or the assignment of training samples. The ALCC method is based on unsupervised classification methods, which is performed over the spectral indices rasters and six Landsat-8 30 m spatial resolution bands. The method was tested in three different study areas. Furthermore, all three study areas were classified by common supervised classification methods, namely, the Maximum Likelihood Classification (MLC) and the Random Forests (RF) method. For comparison accuracy, assessment of the three applied classification methods, namely, the figure of merit, overall agreement, omission and commission, were used. The results show that the overall agreement of the new automatic classification method for the Rijeka, Zagreb and Sarajevo study areas is 90.0%, 89.5% and 89.9%, respectively, and the overall agreement always falls between the overall agreement of the MLC method (88.1%, 88.9% and 86.7%, respectively) and the overall agreement of the RF method of classification (91.7%, 90.4% and 90.2%, respectively). These results confirm that this new automatic, cost-effective and accurate land cover classification method can be easily applied for numerous remote sensing applications.

Landsat-8 ; Unsupervised classification ; k-means ; Spectral indices

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Podaci o izdanju

76 (4)

2019.

1-10

objavljeno

0198-9715

1873-7587

10.1016/j.compenvurbsys.2019.03.001

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