An end-user-oriented framework for the classification of multitemporal SAR images |
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Authors: | D. Amitrano G. Di Martino A. Iodice D. Riccio G. Ruello |
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Affiliation: | 1. Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italydonato.amitrano@unina.it;3. Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy |
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Abstract: | In this article, we present an end-user-oriented framework for multitemporal synthetic aperture radar (SAR) data classification. It accepts as input the recently introduced Level-1α products, whose peculiarities are a high degree of interpretability and increased class separability with respect to single greyscale images. These properties make the Level-1α products very attractive in the application of simple supervised classification algorithms. Specifically, (1) the high degree of interpretability of the maps makes the training phase extremely simple; and (2) the good separation between classes gives excellent results using simple discrimination rules. The end product is a simple, fast, accurate, and repeatable framework. |
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