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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.


Image difference band 3.
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f13-sensors-08-01128: Image difference band 3.

Mentions: We used image differences of Landsat TM reflectance data acquired on June 26, 1998 and May 12, 2000. We selected all image difference bands as the first set of image difference bands for the identification of damaged areas. Tornado damage areas appear evident in bands of 3, 5, and 7 on the differencing image, we layer stacked the above image differences bands as the second set of images for the assessment. Figures 11, 12, 13, 14, 15, and 16 show image difference bands of 1, 2, 3, 4, 5, and 6 respectively. It can be seen from Figure 14 that image difference band 4 does not show much information on damaged areas. By visual judgment of the difference images, we observed that the second least effective image difference band was band 1.


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

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

f13-sensors-08-01128: Image difference band 3.
Mentions: We used image differences of Landsat TM reflectance data acquired on June 26, 1998 and May 12, 2000. We selected all image difference bands as the first set of image difference bands for the identification of damaged areas. Tornado damage areas appear evident in bands of 3, 5, and 7 on the differencing image, we layer stacked the above image differences bands as the second set of images for the assessment. Figures 11, 12, 13, 14, 15, and 16 show image difference bands of 1, 2, 3, 4, 5, and 6 respectively. It can be seen from Figure 14 that image difference band 4 does not show much information on damaged areas. By visual judgment of the difference images, we observed that the second least effective image difference band was band 1.

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.