Automatic CRP mapping using nonparametric machine learning approaches

This paper studies an uneven two-class unsupervised classification problem of satellite imagery, i.e., the mapping of U.S. Department of Agriculture's (USDA) Conservation Reserve Program (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term, resource conserving covers to improve soil, water, and wildlife resources. With recent payments of nearly US $1.6 billion for new enrollments (2002 signup), it is imperative to obtain accurate digital CRP maps for management and evaluation purposes. CRP mapping is a complex classification problem where both CRP and non-CRP areas are composed of various cover types. Two nonparametric machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVMs) are implemented in this work. Specifically, considering the importance of CRP classification sensitivity, a new DTC pruning method is proposed to increase recall. We also study two SVM relaxation approaches to increase recall. Moreover, a localized and parallel framework is suggested in order to efficiently deal with the large-scale CRP mapping need. Simulation results validate the applicability of the suggested framework and proposed techniques.