Our research on remote sensing data analysis is conducted in the context of the USDA’s Conservation Reserve Program (CRP) that seeks to encourage farmland owners to adopt sustainable management practices. There are two CRP-related research issues that involves remotely sensed Landsat imagery, i.e., CRP mapping and compliance monitoring. The first one focuses on the delineation of CRP tracts from the remote sensing data, and the second one is concerned with the compliance of given CRP tracts. We have applied both the decision tree and support vector machine (SVM) approaches to the first problem, and we have shown the benefit to involve the multi-source GIS data for CRP mapping. We also develop a new one-class SVM method for CRP compliance monitoring that modifies the original one-class SVM approach by introducing a self-supervised training scheme.

 
Detected CRP tracts in the Texas County, Oklahoma imposed on the Landsat TM imagery (left) and the reference CRP map (right).

Related Publications

• X. Song, G. Fan and M. Rao, “Edited-Bootstrapped Support Vector Machines for One-class Remote Sensing Data Analysis”, IEEE Geoscience and Remote Sensing Letters, April 2008.

• X. Song, G. Fan, and M. Rao, “Automated CRP Mapping using Non-parametric Machine Learning Approaches”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 43, No. 4, pp888-897, April, 2005.

• X. Song, G. Cherian, and G. Fan, “A $\nu$-insensitive SVM Approach for Compliance Monitoring of the Conservation Reserve Program”, IEEE Geoscience and Remote Sensing Letters, Vol. 2, No. 2, pp99-103, April, 2005.

• X. Song, G. Fan, and M. Rao, "Machine Learning Approaches for Multisource Geospatial Data Classification with Application to CRP Mapping in Texas County, Oklahoma", in Proc. IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, NASA Goddard Visitor Center, Washington DC, October 27/28, 2003.