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

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3 Author(s)
Barnes, C.F. ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Savannah, GA ; Fritz, H. ; Jeseon Yoo

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

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:45 ,  Issue: 6 )