Super resolution mapping (SRM) is a widely used technique to address mixed pixel problem in pixel-based classification. Advanced object-based classification will face the similar mixed phenomenon-mixed object that contains different land-cover classes. Currently, most SRM approaches focus on handling mixed pixels in pixel-based classification. Little if any consideration has been given to predict where classes spatially distribute within mixed objects. This article, therefore, proposes a new

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... ect-based super resolution mapping strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, area-to-point kriging method is applied to predict the soft class values of subpixels within each irregular object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object-level is built to determine the optimal class labels of subpixels. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM performed better and generated more land-cover details within mixed objects than the traditional object-based hard classification. Hence, OSRM provides a valuable solution to mixed objects in object-based classification.