M. Klaric, G. Scott, C. Shyu, C. Davis, and K. Palaniappan

IEEE Int. Geoscience and Remote Sensing Symposium, pgs. 2457--2460, 2006

gis, data mining, features, machine learning, visualization, remote sensing, cbir

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Abstract

This paper presents a framework for the efficient retrieval of satellite imagery from large-scale databases. With the ever-expanding volume of imagery being acquired from satellite platforms it has become increasingly important to locate specific areas of interest within a large database of images. Identifying relevant areas within image databases can be thought of as finding the “needle in the haystack” problem; too often for a particular task or application there exist a small number of useful images hidden among millions of images. The motivation behind the work presented here is that through the use of geospatial image retrieval systems, the number of scenes that image analysts must manually examine may be decreased dramatically. By using a geospatial image retrieval system as a tool, analysts no longer must manually examine the entire database of imagery, but instead can limit their search to a subset identified by our retrieval system. The techniques that are introduced in this paper have been developed in our image retrieval system named GeoIRIS: Geospatial Information Retrieval and Indexing System.