Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration Algorithm

• Published in: IEEE transactions on geoscience and remote sensing Vol. 49; no. 12; pp. 4687 - 4703
• Main Authors: Sjahputera, O., Scott, G. J., Claywell, B. C., Klaric, M. N., Hudson, N. J., Keller, J. M., Davis, C. H.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01-12-2011; Institute of Electrical and Electronics Engineers
• Subjects: Applied geophysics; Change detection; Change detection algorithms; clustering; Clustering algorithms; competitive agglomeration (CA); Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; fuzzy c-means (FCM); Fuzzy logic; high-resolution satellite imagery; Histograms; Internal geophysics; Satellites; satellites; algorithms; detection; Change detection; competitive agglomeration (CA); aggregation; global; high-resolution satellite imagery; fuzzy c-means (FCM); exploitation; imagery; rank; growth; clustering; Pixel; high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2011.2152847

The Geospatial Change Detection and exploitation (GeoCDX) is a fully automated system for detection and exploitation of change between multitemporal high-resolution satellite and airborne images. Overlapping multitemporal images are first organized into 256 m × 256 m tiles in a global grid reference system. The system quantifies the overall amount of change in a given tile with a tile change score as an aggregation of pixel-level changes. The tiles are initially ranked by these change scores for retrieval, review, and exploitation in a Web-based application. However, the ranking does not account for the wide variety of change types that are typically observed in the top-ranked change tiles. To automatically organize the wide variety of change patterns observed in multitemporal high-resolution imagery, we perform tile clustering using the competitive agglomeration (CA) algorithm stabilized using the fuzzy c-means (FCM) algorithm. Each resulting cluster contains tiles with a visually similar type of change. By visual inspection of these tile clusters, GeoCDX users can quickly find certain types of change without having to sift through a large number of tiles initially organized solely by their tile change score, thereby reducing the time it takes for users to discover and exploit the change pattern(s) of greatest interest to a given application (e.g., urban growth, disaster assessment, facility monitoring, etc.). The tile clusters also provide a high-level overview of the various types of change that occur between the two observations. This overview is compared with a similar yet more limited view offered by a relevance feedback tool that requires a user to select sample tiles for use as samples in the reranking process.