A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery
Remote SensingPublished 30 November 2009Open access
Nicola Crocetto, Eufemia Tarantino
Citations25
SJR quartileQ1
SJR score1.02
SNIP1.30
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TL;DR
A hybrid classification method is proposed, adopting the best features extraction strategy for each land cover class on multidate ASTER data, to overcome problems of misclassification among classes with similar spectral response.
Abstract
In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multidate ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multidate ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area.
Keywords
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