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We study an automatic compliance monitoring approach for U.S. Department of Agriculture's (USDA) Conservation Reserve Program (CRP). CRP compliance monitoring checks each CRP tract regarding its contract stipulations, and is formulated as an unsupervised classification of Landsat imageries given the CRP reference data. Assuming the majority of a CRP tract is compliant, we want to locate the non-CRP outliers. A one-class support vector machine (OCSVM) is used to separate minor outliers (non-CRP) from the majority (CRP). ν is an important OCSVM parameter that controls the percentage of outliers and is unknown here. Usually, ν estimation may be complicated or computationally expensive. We propose a ν-insensitive approach by incorporating both the OCSVM and two-class support vector machine (TCSVM) sequentially. Specifically, support vector machine scores obtained from the OCSVM, which indicate the distances between data samples and the classification hyperplane in a feature space, are used to select sufficient and reliable training samples for the TCSVM. Simulation results show the effectiveness and robustness of the proposed method.