Simulated data were used to investigate the relationships between image properties and change detection accuracy in a systematic manner. The image properties examined were class separability, radiometric normalization and image spectral band-to-band correlation. The change detection methods evaluated were post-classification comparison, direct classification of multidate imagery, image differencing, principal component analysis, and change vector analysis. The simulated data experiments showed that the relative accuracy of the change detection methods varied with changes in image properties, thus confirming the hypothesis that caution should be used in generalizing from studies that use only a single image pair. In most cases, direct classification and post-classification comparison were the least sensitive to changes in the image properties of class separability, radiometric normalization error and band correlation. Furthermore, these methods generally produced the highest accuracy, or were amongst those with a high accuracy. PCA accuracy was highly variable; the use of four principal components consistently resulted in substantial decreased classification accuracy relative to using six components, or classification using the original six bands. The accuracy of image differencing also varied greatly in the experiments. Of the three methods that require radiometric normalization, image differencing was the method most affected by radiometric error, relative to change vector and classification methods, for classes that have moderate and low separability. For classes that are highly separable, image differencing was relatively unaffected by radiometric normalization error. CVA was found to be the most accurate method for classes with low separability and all but the largest radiometric errors. CVA accuracy tended to be the least affected by changes in the degree
OPEN ACCESSRemote Sens. 2010, 2 1509 of band correlation in situations where the class means were moderately dispersed, or clustered near the diagonal. For all change detection methods, the classification accuracy increased as simulated band correlation increased, and direct classification methods consistently had the highest accuracy, while PCA generally had the lowest accuracy.
SUMMARY
Considerable bridge‐ground interaction effects are involved in evaluating the consequences of liquefaction‐induced deformations. Due to seismic excitation, liquefied soil layers may result in substantial accumulated permanent deformation of sloping ground near the abutments. Ultimately, global response is dictated by the bridge‐ground interaction as an integral system. However, a holistic assessment of such response generally requires a highly demanding full three‐dimensional (3D) model of the bridge and surrounding ground. As such, in order to capture a number of the salient involved mechanisms, this study focuses on the longitudinal seismic performance of a simpler idealized configuration, motivated by details of an existing bridge‐ground configuration. In this model, a realistic multilayer soil profile is considered with interbedded liquefiable/nonliquefiable strata. The effect of the resulting liquefaction‐induced ground deformation is explored. Attention is given to overall deformation of the bridge structure due to lateral spreading in the vicinity of the abutments. The derived insights indicate a need for such global analysis techniques, when addressing the potential hazard of liquefaction and its consequences.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.