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

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.