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This paper studies a specific one-class classification problem where the training data are corrupted by significant outliers. Specifically, we are interested in the one-class support vector machine (OCSVM) approach that normally requires good training data. However, perfect training data are usually hard to obtain in most real-world applications due to the inherent data variability and uncertainty. To address this issue, we propose an OCSVM-based data editing and classification method that can iteratively purify the training data and learn an appropriate classifier from the trimmed training set. The proposed method is compared with a general OCSVM approach trained from two types of bootstrap samples, and applied to the mapping and compliance monitoring tasks for the U.S. Department of Agriculture's Conservation Reserve Program using remotely sensed imagery. Experimental results show that the proposed method outperforms the general OCSVM using bootstrap samples at a lower computational load.