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Land cover update by supervised classification of segmented ASTER images

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Abstract:

The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multi-spectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52–2.43?µm. The image was initially ortho-rectified and segmented into 51?186 objects, with an average object size of 135?pixels (about 3?ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K-NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6% for the fuzzy classifier, 70.5% for the SVM, 60.9% for the K-NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi-spectral space defined by the nine ASTER bands used.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160412331291233

Affiliations: Faculdade de Ciências, Universidade do Porto, Departamento de Matemática Aplicada, Rua do Campo Alegre, 687, 4169‐007 Porto, Portugal

Publication date: April 1, 2005

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