Limits...
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 a image difference bands 3, 5, and 7 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


getmorefigures.php?uid=PMC3927505&req=5

f26-sensors-08-01128: An output map of a image difference bands 3, 5, and 7 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.

Mentions: It was evident from the visual analysis that image difference bands 3, 5, and 7 showed damaged areas with lower spatial variances than other image difference bands, we anticipated that a composite image difference of the original reflectance bands 3, 5, and 7 would produce a satisfactory outcome. From Tables 7 and 8, overall accuracies produced by the above composite of image difference bands 3, 5, and 7 using the unsupervised classifier and supervised classifier did not produce satisfactory accuracies (i.e., 70.83%, 78.33%) as expected. Both outputs (Figures 25 and 26) were similar to the outputs from the previous image difference bands. They also show a lower noise level in non-damaged areas and contain less damaged areas.


Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
An output map of a image difference bands 3, 5, and 7 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

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

f26-sensors-08-01128: An output map of a image difference bands 3, 5, and 7 using a supervised approach (i.e., maximum likelihood). Note: blue color represents non-damaged areas and red color represents damaged areas.
Mentions: It was evident from the visual analysis that image difference bands 3, 5, and 7 showed damaged areas with lower spatial variances than other image difference bands, we anticipated that a composite image difference of the original reflectance bands 3, 5, and 7 would produce a satisfactory outcome. From Tables 7 and 8, overall accuracies produced by the above composite of image difference bands 3, 5, and 7 using the unsupervised classifier and supervised classifier did not produce satisfactory accuracies (i.e., 70.83%, 78.33%) as expected. Both outputs (Figures 25 and 26) were similar to the outputs from the previous image difference bands. They also show a lower noise level in non-damaged areas and contain less damaged areas.

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.