Ensemble Remote Sensing Classifier Based on a-Torrent Rough Set Feature Partition
SuLi Zhang, Xin Pan · 2010
Supervised classification in remote sensing imagery is receiving increasing attention in current research. In order to improve the classification ability, a lot of spatial-features (e.g., texture information generated by GLCM) have been utilized. Unfortunately, too many features often cause classifier over-fit to a certain features' character and lead to lower classification accuracy. The traditional feature selection algorithms have an unstable classification performance which depends on the number of training samples. This study presents a α-torrent rough set based ensemble remote sensing image classifier. It partition feature set into a lot of reducts,and constructs training subset by utilizing these reducts. Each training subset trains an artificial neural network (ANN) classifier; the decisions from all the base classifiers are combined with a voting strategy. This approach can reduce input features to a single classifier, and it can avoid bias caused by feature selection. The classifier has been compared with the direct ANN method and the traditional feature selection method. It can be seen from the result that our method has better classification accuracy and more stable than the others.