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Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

View Article: PubMed Central - PubMed

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 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.

No MeSH data available.


An output map of PC composite bands 3 and 4 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.
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f22-sensors-08-01128: An output map of PC composite bands 3 and 4 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.

Mentions: Overall accuracies produced by a composite image of PC bands 3 and 4 using the unsupervised approach and supervised approach were 85.00% (Table 3) and 87.50% (Table 4) respectively. Both producer's and user's accuracies for non-damaged areas were higher than damaged areas. This is probably due to the fact that area extent of non-damaged category was a lot larger than damaged areas, and many randomly selected points fall in non-damaged areas. The overall accuracies of 85% and 87.5% produced by a composite image of PC bands 3 and 4 reach the minimum mapping accuracy of 85% required for most resource management applications [26-27]. Figures 21 and 22 suggest that the output generated by the supervised approach shows more damaged areas whereas the unsupervised output contains less damaged areas. It has been found that PC composite of bands 3 and 4 with the use of either an unsupervised or a supervised approach can be considered an effective approach for identifying damaged areas due to a disaster event.


Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
An output map of PC composite bands 3 and 4 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3927505&req=5

f22-sensors-08-01128: An output map of PC composite bands 3 and 4 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.
Mentions: Overall accuracies produced by a composite image of PC bands 3 and 4 using the unsupervised approach and supervised approach were 85.00% (Table 3) and 87.50% (Table 4) respectively. Both producer's and user's accuracies for non-damaged areas were higher than damaged areas. This is probably due to the fact that area extent of non-damaged category was a lot larger than damaged areas, and many randomly selected points fall in non-damaged areas. The overall accuracies of 85% and 87.5% produced by a composite image of PC bands 3 and 4 reach the minimum mapping accuracy of 85% required for most resource management applications [26-27]. Figures 21 and 22 suggest that the output generated by the supervised approach shows more damaged areas whereas the unsupervised output contains less damaged areas. It has been found that PC composite of bands 3 and 4 with the use of either an unsupervised or a supervised approach can be considered an effective approach for identifying damaged areas due to a disaster event.

View Article: PubMed Central - PubMed

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 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.

No MeSH data available.