Boosting a wavelet packet transform based landmine detector

We consider landmine detection using forward-looking ground penetrating radar (FLGPR), which is quite challenging due to the weak signal returns of landmines. The two main challenging tasks include extracting intricate structures of the target signals from the radar imagery and adapting the classifier to the surrounding environment through learning. Through the time-frequency analysis, we find that the most discriminant information is time-frequency localized. This observation motivates us to use the wavelet packet transform to sparsely represent the signals with the discriminant information encoded into several bases. Then the sequential floating forward selection method is used to extract these components and thereby a neural network classifier is designed. To further improve the classification performance, the AdaBoost algorithm is used. We modify the original AdaBoost algorithm to integrate the feature selection process into each iteration. Experimental results based on measured FLGPR data are presented, showing that with the proposed classifier, a significant improvement on both the training and the testing performances can be achieved.