Use this identifier to quote or link this document:

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon
Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximiliem; Macía, Manuel; Reyes-García, Victoria
Institut de Ciència i Tecnologia Ambientals; Universitat Autònoma de Barcelona
Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.
504 - Ciències del medi ambient
Sòls, Ús dels -- Bolívia
Sòls, Ús dels -- Classificació
Cartografia digital
Prospecció de dades
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:
25 p.
Working Paper
Working Papers on Environmental Sciences;

Full text files in this document

Files Size Format
WorkPapEnvSci_2011_05.pdf 790.0 KB PDF

Show full item record

Related documents

Other documents of the same author

André, Michel; Manuel Lázaro, Antonio; Dañobeitia, Juan José; Rolin, Jean-François; Person, Roland
Mitrovic, Sandra; Nogueira, Cristina; Cantero Recasens, Gerard, 1984-; Kiefer, Kerstin; Fernández-Fernández, José Manuel; Popoff, Jean-François; Casano, Laetitia; Bard, Frederic A.; Gómez, Raul; Valverde, M. A. (Miguel Ángel), 1963-; Malhotra, Vivek
Facciolo Furlan, Gabriele; Almansa, Andrés; Aujol, Jean-François; Caselles, Vicent