title: Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression.
abstract: Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%.
authors.affiliation: Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200 Orestiada, Greece. gmallin@fmenr.duth.gr.
authors.forename: Georgios
authors.initials: G
authors.lastname: Mallinis
authors.affiliation: Department of Environmental and Natural Resources Management, University of Ioannina, Seferi 2, GR-30100 Agrinio, Greece. nkoutsia@cc.uoi.gr.
authors.forename: Nikos
authors.initials: N
authors.lastname: Koutsias
keywords: Land cover mapping; autocovariate; logistic regression; texture