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Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Jun2007, Vol. 45 Issue 6, p1631-1640. 10p. 4 Diagrams, 3 Charts, 2 Graphs. - Publication Year :
- 2007
-
Abstract
- Detection, classification, and attribution of high- resolution satellite image features in nearshore areas in the aftermath of Hurricane Katrina in Gulfport, MS, are investigated for damage assessments and emergency response planning. A system-level approach based on image-driven data mining with σ-tree structures is demonstrated and evaluated. Results show a capability to detect hurricane debris fields and storm-impacted nearshore features (such as wind-damaged buildings, sand de- posits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The σ-tree-based image information mining capability is demonstrated to be useful in disaster response planning by detecting blocked access routes and autonomously discovering candidate rescue/recovery staging areas. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 45
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 25447628
- Full Text :
- https://doi.org/10.1109/TGRS.2007.890808