spectral and spatial-based classification for broad-scale land cover mapping based on logistic regression光谱和spatial-based为大规模土地覆盖分类映射基于逻辑回归.pdfVIP

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spectral and spatial-based classification for broad-scale land cover mapping based on logistic regression光谱和spatial-based为大规模土地覆盖分类映射基于逻辑回归.pdf

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spectral and spatial-based classification for broad-scale land cover mapping based on logistic regression光谱和spatial-based为大规模土地覆盖分类映射基于逻辑回归

Sensors 2008, 8, 8067-8085; DOI: 10.3390/s8128067 OPEN ACCESS sensors ISSN 1424-8220 /journal/sensors Article Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression Georgios Mallinis 1 and Nikos Koutsias 2,* 1 Department of Forestry Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200 Orestiada, Greece; E-Mail: gmallin@fmenr.duth.gr 2 Department of Environmental and Natural Resources Management, University of Ioannina, Seferi 2, GR-30100 Agrinio, Greece * Author to whom correspondence should be addressed; Tel.: +30 26410 74201; Fax: +30 26410 74179; E-Mail: nkoutsia@cc.uoi.gr Received: 9 October 2008; in revised form: 5 November 2008 / Accepted: 17 November 2008 / Published: 8 December 2008 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 a