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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.


Principal component composite band 3.
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f5-sensors-08-01128: Principal component composite band 3.

Mentions: The PCA results suggest that tornado damage areas can be observed in principal component (PC) bands 2, 3, and 4 and hence, we layer stacked PC bands 2, 3, and 4 as the first set of images for the assessment. Figures 3, 4, 5, 6, 7, and 8 show PC bands 1, 2, 3, 4, 5, and 6 respectively. Even though it is understood that the first principal component contains the largest component of the total scene variance [18], it can be observed from Figure 1 that the first PC band does not show damaged areas of the 3 May 1999 tornado well. PC5 and other principal components at higher orders do not have much information on changes except some noise in the images. A higher number of succeeding component images may contain a decreasing percentage of the total scene variance. Hence, we do not show higher level of PC images after PC-6. A closer inspection revealed that PC bands 3 and 4 showed the strongest response of tornado damage signatures in the images. We anticipated that this could potentially lead to a good result and we layer stacked PCA bands 3 and 4 as an additional sets of PCA images for the analysis. We selected training samples of damaged areas and non-damaged areas to perform a supervised classification approach (i.e., maximum likelihood) in the 2 selected composite of PC image bands (i.e., PC 2, 3, 4 and PC 3, 4). Figures 9 and 10 show the first set of PC composite bands 2, 3, 4, and PC composite bands 3, 4.


Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
Principal component composite band 3.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927505&req=5

f5-sensors-08-01128: Principal component composite band 3.
Mentions: The PCA results suggest that tornado damage areas can be observed in principal component (PC) bands 2, 3, and 4 and hence, we layer stacked PC bands 2, 3, and 4 as the first set of images for the assessment. Figures 3, 4, 5, 6, 7, and 8 show PC bands 1, 2, 3, 4, 5, and 6 respectively. Even though it is understood that the first principal component contains the largest component of the total scene variance [18], it can be observed from Figure 1 that the first PC band does not show damaged areas of the 3 May 1999 tornado well. PC5 and other principal components at higher orders do not have much information on changes except some noise in the images. A higher number of succeeding component images may contain a decreasing percentage of the total scene variance. Hence, we do not show higher level of PC images after PC-6. A closer inspection revealed that PC bands 3 and 4 showed the strongest response of tornado damage signatures in the images. We anticipated that this could potentially lead to a good result and we layer stacked PCA bands 3 and 4 as an additional sets of PCA images for the analysis. We selected training samples of damaged areas and non-damaged areas to perform a supervised classification approach (i.e., maximum likelihood) in the 2 selected composite of PC image bands (i.e., PC 2, 3, 4 and PC 3, 4). Figures 9 and 10 show the first set of PC composite bands 2, 3, 4, and PC composite bands 3, 4.

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.