Purified Algorithm for Training Samples Based on Automatic Searching and Spectral Matching Technique

A purified algorithm for training samples based on local automatic searching and spectral matching technique is presented.In the first step,the optimal training sites are searched and selected automatically by taking advantage of local spatial information in remote sensed images.The selected training sites are then purified further in spectral space using spectral matching technique.The proposed algorithm is capable of purifying training samples with spatial and spectral information.Experimental results are given to show that supervised classification with the proposed purification algorithm has superiority capability over the traditional supervised classification without purifying training sites on visual judgment and accuracy assessment.