A new approach to contextual learning using interval arithmetic and its applications for land-use classification

Abstract

Contextual-based classification has been paramount in the last years, since spatial and temporal information play an important role during the process of learning the behavior of the data. Sequential learning is also often employed in this context in order to augment the feature vector of a given sample with information about its neighborhood. However, most part of works describe the samples using features obtained through standard arithmetic tools, which may not reflect the data as a whole. In this work, we introduced the Interval Arithmetic to the context of land-use classification in satellite images by describing a given sample and its neighbors using interval of values, thus allowing a better representation of the model. Experiments over four satellite images using two distinct supervised classifiers showed we can considerably improve sequential learning-oriented pattern classification using concepts from Interval Arithmetic. (C) 2016 Elsevier B.V. All rights reserved.