Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery

Author

Barnes, Christopher F. ; Fritz, Hermann ; Yoo, Jeseon

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Savannah, GA

Volume

45

Issue

6

fYear

2007

fDate

6/1/2007 12:00:00 AM

Firstpage

1631

Lastpage

1640

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 sigma-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 deposits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The sigma-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

Keywords

data mining; disasters; geography; image classification; storms; terrain mapping; Gulfport; Hurricane Katrina; Massachusetts; data mining; disaster assessment; emergency response planning; image attribution; image classification; image detection; satellite imaging; sigma-tree structure; Buildings; Data mining; Data warehouses; Hurricanes; Image analysis; Rail transportation; Reconnaissance; Satellites; Vegetation mapping; Water storage; $sigma$-tree classifiers; Emergency response planning; image information mining; image-driven data mining; satellite image hurricane disaster assessments;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/TGRS.2007.890808

Filename

4215029