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Multi-feature optimization strategies for target classification using seismic and acoustic signatures

Perimeter monitoring systems have become one of the most researched topics in recent times. Owing to the increasing demand for using multiple sensor modalities, the data for processing is becoming high dimensional. These representations are often too complex to visualize and decipher. In this paper, we will investigate the use of feature selection and dimensionality reduction strategies for the classification of targets using seismic and acoustic signatures. A time-slice classification approach with 43 numbers of features extracted from multi-domain transformations has been evaluated on the SITEX02 military vehicle dataset consisting of tracked AAV and wheeled DW vehicle. Acoustic signals with SVM-RBF resulted in an accuracy of 93.4%, and for seismic signals, the ensemble of decision trees classifier with bagging approach resulted in an accuracy of 90.6 %. Further principal component analysis (PCA) and neighborhood component analysis (NCA) based feature selection approach has been applied to the extracted features. NCA based approach retained only 20 features that obtained classification accuracy ~ 94.7% for acoustic and ~ 90.5% for seismic. An increase of ~2% to 4% is observed for NCA when compared to PCA based feature transformation approach. A further fusion of individual seismic and acoustic classifier posterior probabilities increases the classification accuracy to 97.7%. Further, a comparison with PCA and NCA based feature optimization strategies have also been validated on CSIO experimental datasets comprising of moving civilian vehicles and anthropogenic activities.