A fuzzy segmentation tool for remote sensing data

Abstract

Remote sensing data are an important source of information for a variety of applications, such as coastal mapping applications, monitor land use, and chart wildlife habitats, for example. One of the most important task for these data analysis is the segmentation. Segmentation means the action of merging neighbouring pixels into segments (or regions), based on their homogeneity or heterogeneity parameters. Traditional image segmentation methods looks for delineating discrete image objects with sharp edges, which cannot be always possible, mainly considering that many geographic objects, both natural and man-made, may not appear clearly bounded in remotely sensed images. A fuzzy approach seems natural in order to capture the structure of objects in the image and takes into account the fuzziness of the real world and the ambiguity of remote sensing imagery. The main goal of this work is define boundaries of objects in an image. This proposal aims to be faster than other segmentation approaches inside the TerraLib tools by considering only the neighbourhood of a selected pixel. This work proposes the use of image's tone and colour to select and define objects in remote scenes based on fuzzy rules. The fuzzy set is defined by an input tolerance level, which can be adjustable according to the desired granularity of the selection. The proposal methodology is not limited by the selection of only one object, that is, the mask can be designed by a set of objects with different features and tolerances. The algorithm also returns the objects size and proportion. The quality of the individual segmentation results is evaluated based on multi-spectral Landsat 5-TM, Landsat 7-ETM+ and CBERS data. This is done by visual comparison, which is supplemented by a detailed investigation using visual interpreted reference areas.