Seafloor Segmentation Using Combined Texture Features of Sidescan Sonar Images

In this paper, an unsupervised seafloor segmentation method using combined texture features of sidescan sonar images is proposed. Two sets of features are considered in the proposed algorithm. One calculates the statistics from the gray-level co-occurrence matrix (GLCM), and the other obtains the statistics in the nonsubsampled contourlet transform domain (NSCT). The two sets of features are combined together to produce a multi-dimensional feature vector for each pixel. Principal component analysis (PCA) is used to reduce the dimensionality of each feature vector. The Silhouette index is adopted to automatically estimate the number of seafloor types in sonar images. The segmentation is achieved using k-means clustering based on the compact feature vectors. Experimental results show that the proposed method can improve the seafloor segmentation accuracy.