Abstract
Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified 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 damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.
Highlights
Remote sensing is a cost effective tool for large scale damage surveys after hazardous events
All image processing techniques for damaged area detection assume that damaged area and undamaged area relate to discernible differences in spectral reflectance on images
Overall accuracies produced by a composite image of principal component (PC) bands 2, 3, and 4 using the unsupervised approach (i.e., iterative self organizing data analysis (ISODATA)) and supervised approach were 84.17% (Table 1) and 79.17% (Table 2) respectively
Summary
Remote sensing is a cost effective tool for large scale damage surveys after hazardous events. Satellite or airborne imagery provides an immediate overview of the damaged area and facilitate rescue and recovery efforts. These images can provide damage estimates, identified damaged area from the imagery can guide limited emergency or survey crews to needed areas for detailed analysis. This paper compares three main approaches in remote sensing image processing and through the comparison to draw insights into the strengths and limitations of each technique in detecting tornado damage tracks. All image processing techniques for damaged area detection assume that damaged area and undamaged area relate to discernible differences in spectral reflectance on images. Classification of imagery reflectance reveals classes of damaged and undamaged areas
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