Using Texture to Annotate Remote Sensed Datasets
 

Dept. of Electrical and Computer Engineering
University of California at Santa Barbara
Santa Barbara, CA 93106
Email: {snewsam,lwang,sitaram,manj} @ece.ucsb.edu

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 way. 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.