Unsupervised seabed segmentation of synthetic aperture sonar imagery via wavelet features and spectral clustering

An unsupervised seabed segmentation algorithm for synthetic aperture sonar (SAS) imagery is proposed. Each 2 m x 2 m area of seabed is treated as a unique data point. A set of features derived from the coefficients of a wavelet decomposition are extracted for each data point. Spectral clustering is then performed with this data, which assigns the data points to clusters. This clustering result is then used directly to effect a segmentation of the SAS image into different seabed types. Experimental results on four real, measured SAS images demonstrate the promise of the proposed approach. Importantly, accurate image segmentation results are achieved on the large, challenging images without the aid of any training data or parameter estimation.