| This research focuses on two specific remote sensing problems associated with the United States Department of Agriculture (USDA)'s Conservation Reserve Program (CRP). Specifically, there are two essential needs pertaining to CRP management and evaluation, i.e., CRP compliance monitoring and CRP mapping. We invoke approaches based on the Support Vector Machine (SVM) to address these two issues where two classes, CRP and non-CRP, are involved for data classification. The SVM is a recently developed supervised classifier aiming at maximizing the margin between two clusters in a projected feature space. The SVM was also adapted to solve one-class problems, i.e., novelty detection. The one-class SVM (OCSVM) has a parameter nu to control the percentage of outliers or minority data.; We propose two SVM-based methods for CRP compliance monitoring. The first method uses OCSVMs trained with different nu values and selects the one producing the maximum margin between clusters in the projected space. Then a two-class SVM (TCSVM) is trained by using the initial OCSVM classification results. The second method only involves the OCSVM training once. (Abstract shortened by UMI.)... |
