A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Seismic signal is widely used in ground target classification due to its inherent characteristics. However, its propagation is highly dependent on local underlying geology. It means that nearly every one geographical environment requires a unique classifier. To resolve the problem, this paper presents a robust feature extraction method Log-Sigmoid Frequency Cepstral Coefficients (LSFCC) which evolves from Mel frequency cepstral coefficients (MFCC) for ground target classification by means of geophone. With the LSFCCs, the average classification accuracy of tracked and wheeled vehicle is more than 89% in three different geographical environments by only one classifier which is trained in one of the three environments.