A Comparative Study of Sample Selection Strategies Based on Optimum Experimental Design for SAR Image Classification

Abstract

In this paper, we evaluate sample selection strategies based on optimum experimental design for SAR image classification. Traditionally, support vector machine active learning is widely used by selecting the samples close to the decision surface. Recently, new methods based on optimum experimental design have been developed. To gain a complete understanding of these selection strategies, a comparative study on three approaches, transductive experimental design, manifold adaptive experimental design and locally linear reconstruction, has been performed for SAR image classification using different features. Among the three approaches,we show that manifold adaptive experimental design performs best and stably in terms of both accuracy and computational complexity.