A K-Means Remote Sensing Image Classification Method Based On AdaBoost

A remote sensing image classification method is presented based on AdaBoost algorithm in this paper. To solve the resampling of patterns, a weighted version is provided. The detail of implementation about the boosting algorithm is presented as well as experiments of the application on k-means, which proves the effectiveness of the implementation proposed in this paper. Further more, classification results produced by the boosted k-means present an obvious advantage on the elimination of isolated points and recognition of slim objects, when compared with the basic k-means.