Entropy-Balanced Bitmap Tree for Shape-Based Object Retrieval From Large-Scale Satellite Imagery Databases

• Published in: IEEE transactions on geoscience and remote sensing Vol. 49; no. 5; pp. 1603 - 1616
• Main Authors: Scott, G J, Klaric, M N, Davis, C H, Chi-Ren Shyu
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-05-2011; Institute of Electrical and Electronics Engineers
• Subjects: Applied geophysics; Content-based retrieval; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; Geospatial analysis; image databases; Indexing; Internal geophysics; knowledge-based indexing; object indexing; remotesensing; Satellites; Shape; satellites; efficiency; entropy; imagery; remote sensing; Content-based retrieval; knowledge-based indexing; data bases; trees; image databases; object indexing; high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2010.2088404

In this paper, we present a novel indexing structure that was developed to efficiently and accurately perform content-based shape retrieval of objects from a large-scale satellite imagery database. Our geospatial information retrieval and indexing system, GeoIRIS, contains 45 GB of high-resolution satellite imagery. Objects of multiple scales are automatically extracted from satellite imagery and then encoded into a bitmap shape representation. This shape encoding compresses the total size of the shape descriptors to approximately 0.34% of the imagery database size. We have developed the entropy-balanced bitmap (EBB) tree, which exploits the probabilistic nature of bit values in automatically derived shape classes. The efficiency of the shape representation coupled with the EBB tree allows us to index approximately 1.3 million objects for fast content-based retrieval of objects by shape.