An adaptive approach for simultaneous classification of remote sensing scenes including rural and urban targets

Abstract

ABSTRACT In this paper, an automatic adaptive image classification framework designed to operate in multiresolution scenes including rural and urban targets is proposed and tested. Traditional image analysis is commonly aimed to classify images using a single strategy and source of data over the entire scene. Ideally, urban targets should predict specialized classification systems using high spatial resolution images, such as object-based image analysis and non-parametric classifiers. Conversely, rural targets should be handled with the high-spectral resolution, pixel-based classification approaches, and parametric techniques. The formulation proposed in this study starts by performing an prior separation of rural and urban areas by assuming Central Limit Theorem (CLT) establishments. Then, both kinds of targets are labelled in an automatic adaptive fashion, each one with proper data and method previously selected. One experiment performed using set of data composed by a high spatial resolution true-colour image and a multispectral image, as well as preselected classification techniques particularly adjusted for each case. Visual and quantitative assessing by two accuracy metrics testing the proposed approach versus traditional classification confirm the soundness of the proposed framework.