Comparison of Learned versus Engineered Features for Classification of Mine Like Objects from Raw Sonar Images

Advances in high frequency sonar have provided increasing resolution of sea bottom objects, providing higher fidelity sonar data for automated target recognition tools. Here we investigate if advanced techniques in the field of visual object recognition and machine learning can be applied to classify mine-like objects from such sonar data. In particular, we investigate if the recently popular Scale-Invariant Feature Transform (SIFT) can be applied for such high-resolution sonar data. We also follow up our previous approach in applying the unsupervised learning of deep belief networks, and advance our methods by applying a convolutional Restricted Boltzmann Machine (cRBM). Finally, we now use Support Vector Machine (SVM) classifiers on these learned features for final classification. We find that the cRBM-SVM combination slightly outperformed the SIFT features and yielded encouraging performance in comparison to state-of-the-art, highly engineered template matching methods.