An end-user-oriented framework for the classification of multitemporal SAR images. Issue 1 (2nd January 2016) - Amitrano, D.; Di Martino, G.; Iodice, A.; Riccio, D.; Ruello, G.
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-1 α products, whose peculiarities are a high degree of interpretability and increased class separability with respect to single greyscale images. These properties make the Level-1 α 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.