Using texture to analyze and manage large collections of remote sensed image and video data

We describe recent research into using the visual primitive of texture to analyze and manage large collections of remote sensed image and video data. Texture is regarded as the spatial dependence of pixel intensity. It is characterized by the amount of dependence at different scales and orientations, as measured with frequency-selective filters. A homogeneous texture descriptor based on the filter outputs is shown to enable (1) content-based image retrieval in large collections of satellite imagery, (2) semantic labeling and layout retrieval in an aerial video management system, and (3) statistical object modeling in geographic digital libraries.