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Sensors 2008, 8(12), 8067-8085; doi:10.3390/s8128067

Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression

1 Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200 Orestiada, Greece 2 Department of Environmental and Natural Resources Management, University of Ioannina, Seferi 2, GR-30100 Agrinio, Greece
* Author to whom correspondence should be addressed.
Received: 9 October 2008 / Revised: 5 November 2008 / Accepted: 17 November 2008 / Published: 8 December 2008
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
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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%.
Keywords: Land cover mapping; logistic regression; autocovariate; texture Land cover mapping; logistic regression; autocovariate; texture
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mallinis, G.; Koutsias, N. Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression. Sensors 2008, 8, 8067-8085.

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