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Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

Sensors
By: , , , and 
https://doi.org/10.3390/s8021128

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Abstract

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Publication type Article
Publication Subtype Journal Article
Title Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data
Series title Sensors
DOI 10.3390/s8021128
Volume 8
Issue 2
Year Published 2008
Language English
Contributing office(s) Earth Resources Observation and Science (EROS) Center
First page 1128
Last page 1156
Online Only (Y/N) N
Additional Online Files (Y/N) N
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