The AdaBoost Classification of Land-mine Target with Adaptive Feature Selection

In order to solve the land-mine classification problem on a Forward-Looking Ground Penetrating Virtual Aperture Radar (FLGPVAR), a new classification algorithm composed of weak classification iteration and adaptive feature selection is proposed. It is based on traditional AdaBoost algorithm, the feature selection is part of weak classification iterations, and the false alarm is treated as the cost function under constant detection rate. It is proved by real data that the method is applicable to the classification of land-mine and clutter on forward-looking ground penetrating virtual aperture Radar and the performance is better than traditional AdaBoost algorithm.