Texture-based Segmentation for Identification of Geological Units in Remotely Sensed Imagery

This study proposes a segmentation procedure based on multivariate texture to extract spatial objects from an image scene. Object uncertainty is quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, is integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We apply a multivariate extension of the standard univariate LBP operator to describe colour texture [1]. The paper is illustrated with a case study. It considers identification of geological units in ASTER imagery of the Dungovi aimag, Southern Mongolia. The multivariate LBP operator segments the area into meaningful geological objects, yielding valuable information on uncertainty at the transition zones. Geospatial data quality is a topic frequently covered in recent scientific literature on GIS and remote sensing. Important components of data quality are uncertainty and accuracy. Therefore, we focus on suitable accuracy measures to validate the segmentation result.