Iterative support vector machine for hyperspectral image classification

Support vector machine (SVM) has received considerable interest in hyperspectral image classification. In order to make SVM work effectively one challenge is selection of training samples. In supervised classification it is generally done by random sampling for cross validation where two issues must be addressed. One is how many training samples required to allow SVM to produce good performance and the other is how to deal with random selections of training samples which produce inconsistent results. This paper presents a new type of SVM, called iterative SVM (ISVM) to address these two issues. The idea is to implement an SVM iteratively in such a way that the sample size is not necessarily to be large while the random sampling issue can be also resolved. To substantiate the utility of ISVM Purdue data is further used for experiments.