Open-ended texture classification for terrain mapping

This paper introduces a new classification scheme called “open-ended texture classification”. The standard approach for texture classification is to use a closed n-class classifier based on the Bayesian paradigm. These perform supervised classification, whereby all the texture classes have to be predefined. We propose a new texture classification scheme, one that does not require a complete set of predefined classes. Instead our texture classification scheme is based on a significance test. A texture is classified on the basis of whether or not its statistical properties are deemed to be from the same population of statistics as those that define a specific texture class. This new “open-ended texture classification” is considered potentially valuable in the practical application of terrain mapping of synthetic aperture radar (SAR) images