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This work is motivated by the problem of image mis-registration in remotesensing and we are interested in determining the resulting loss in the accuracyof pattern classification. A statistical formulation is given where we proposeto use data contamination to model and understand the phenomenon of imagemis-registration. This model is widely applicable to many other types of errorsas well, for example, measurement errors and gross errors etc. The impact ofdata contamination on classification is studied under a statistical learningtheoretical framework. A closed-form asymptotic bound is established for theresulting loss in classification accuracy, which is less than$\epsilon/(1-\epsilon)$ for data contamination of an amount of $\epsilon$. Ourbound is sharper than similar bounds in the domain adaptation literature and,unlike such bounds, it applies to classifiers with an infiniteVapnik-Chervonekis (VC) dimension. Extensive simulations have been conducted onboth synthetic and real datasets under various types of data contamination,including label flipping, feature swapping and the replacement of featurevalues with data generated from a random source such as a Gaussian or Cauchydistribution. Our simulation results show that the bound we derive is fairlytight.

Classification under Data Contamination with Application to Remote Sensing Image Mis-registration