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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Macchiavello, G. Moser, G. Boni, G. Serpico, S.B. |
| Copyright Year | 2009 |
| Description | Author affiliation: University of Genoa, Dept. of Biophysical and Electronic Eng. (DIBE), via Opera Pia 11a, I-16145 Genoa (Italy) (Moser, G.; Serpico, S.B.) || CIMA Foundation, University Campus, via A. Magliotto 2, I-17100 Savona (Italy) (Macchiavello, G.; Boni, G.) |
| Abstract | The problem of the classification of snow-covered areas from multispectral images is addressed in this paper. The key idea of the proposed technique is to integrate a decision tree classifier (DTC) and a Bayesian unsupervised thresholding algorithm, aiming at a complete automation of the classification process. Given a classification problem, the DTC approach decomposes the problem in a suitable tree-structured collection of binary sub-problems, for which simple (e.g., threshold-based) decision rules can be defined. The proposed strategy, by adopting the tree classification, discriminates several snow-covered and non-snow-covered classes, by decomposing the related multi-class problem into a set of binary thresholding sub-problems involving the multispectral channels and the resulting normalized difference vegetation index and normalized difference snow index. Focusing on a critical node in the tree, a Bayesian approach is used to expresses the threshold-selection problem as the minimization of a functional related to the probability of classification error. Experiments are reported on MODIS data. |
| File Size | 854069 |
| File Format | |
| ISBN | 9781424433940 |
| DOI | 10.1109/IGARSS.2009.5418270 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-12 |
| Publisher Place | South Africa |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Snow Bayesian methods Classification tree analysis Multispectral imaging MODIS Vegetation mapping Histograms Image analysis Clouds Optimization methods unsupervised learning image classification tree data structures |
| Content Type | Text |
| Resource Type | Article |
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