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Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
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 d...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3927505/ https://www.ncbi.nlm.nih.gov/pubmed/27879757 |
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author | Myint, Soe W. Yuan, May Cerveny, Randall S. Giri, Chandra P. |
author_facet | Myint, Soe W. Yuan, May Cerveny, Randall S. Giri, Chandra P. |
author_sort | Myint, Soe W. |
collection | PubMed |
description | 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 object-oriented 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. |
format | Online Article Text |
id | pubmed-3927505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-39275052014-02-18 Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data Myint, Soe W. Yuan, May Cerveny, Randall S. Giri, Chandra P. Sensors (Basel) Full Research Paper 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 object-oriented 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. Molecular Diversity Preservation International (MDPI) 2008-02-21 /pmc/articles/PMC3927505/ /pubmed/27879757 Text en © 2008 by MDPI Reproduction is permitted for noncommercial purposes. |
spellingShingle | Full Research Paper Myint, Soe W. Yuan, May Cerveny, Randall S. Giri, Chandra P. Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title | Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title_full | Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title_fullStr | Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title_full_unstemmed | Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title_short | Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data |
title_sort | comparison of remote sensing image processing techniques to identify tornado damage areas from landsat tm data |
topic | Full Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3927505/ https://www.ncbi.nlm.nih.gov/pubmed/27879757 |
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