Ensemble remote sensing classifier based on rough set theory and genetic algorithm

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 spatial-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 rough set and genetic algorithm based ensemble remote sensing image classifier (briefly denoted as RSGAEC). This approach can reduce input features to a single classifier, and it can avoid bias caused by feature selection. The RSGAEC classifier has been compared with the direct ANN method and the traditional feature selection method. It can be seen from the result that RSEC has better classification accuracy and more stable than the others in remote sensing classification.