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A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery
Department of Civil Engineering, Second University of Naples, via Roma n. 29, Aversa (CE), 81031, Italy
Department of Highway and Transportation, Section Geomatics, Polytechnic University of Bari, via Orabona n.4, Bari, 70125, Italy
* Author to whom correspondence should be addressed.
Received: 23 October 2009; in revised form: 20 November 2009 / Accepted: 26 November 2009 / Published: 30 November 2009
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: class-oriented classification; MAD calibration; TIR ASTER bands; change detection
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MDPI and ACS Style
Crocetto, N.; Tarantino, E. A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery. Remote Sens. 2009, 1, 1171-1189.
Crocetto N, Tarantino E. A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery. Remote Sensing. 2009; 1(4):1171-1189.
Crocetto, Nicola; Tarantino, Eufemia. 2009. "A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery." Remote Sens. 1, no. 4: 1171-1189.