Using texture to annotate remote sensed datasets

Abstract

Texture remains largely underutilized in the analysis of remote sensed datasets compared to descriptors based on the orthogonal spectral dimension. This paper describes our recent efforts towards using texture to automate the annotation of remote sensed imagery. Two applications are described that use the homogeneous texture descriptor recently standardized by MPEG-7. In the first, higher-level access to remote sensed imagery is enabled by using the texture descriptor to model geo-spatial objects. In particular, the common textures, or texture motifs, are characterized as Gaussian mixtures in the high-dimensional feature space. In the second application, the texture descriptor is used to label regions in a large collection of aerial videography in a perceptually meaningful wax. Gaussian mixtures are used to model the distribution of feature vectors for a variety of semantic classes. Frame level similarity retrieval based on semantic layout and semantic histogram is enabled by modeling the spatial arrangement of the labeled regions as a Markov random field.