Semisupervised Change Detection for Bitemporal Images Based on Fast Progressive Transductive SVM

We propose a fast progressive transductive support vector machine (FPTSVM) method for detecting changes in bitemporal multispectral images. Our method differs from other transductive SVM methods in that much fewer unlabelled data vectors need to be considered in each iteration for the determination of transductive samples. As a result, the computational cost of the method is reduced. To improve the change detection results, our method incorporates the spectral angle (SA) as an extra component to each input data vector. Experimental results on Landsat dataset show that our method gives the smallest overall error and the fastest run time.