Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

Abstract

To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantumbehaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

“…These non-shipwreck images are mostly contained different types of pure sea bottom backgrounds and other targets. Most of these images were measured by various types of SSS instruments in China by ourselves, while some shipwreck or other target images were collected from other references [3,10,25,33]. These images were randomly divided in two for the training and testing experiment.…”

“…Shipwreck image and its fractal dimension (D), the raw SSS image is referenced from[33]. The red ellipse marked area demonstrate the shipwreck contour.…”

“…Shipwreck image and its fractal dimension (D), the raw SSS image is referenced from[33]. The red ellipse marked area demonstrate the shipwreck contour.BothFigure 1(D was calculated by the whole image) andFigure 6(D was calculated by the neighborhoods in one image) show the validity and defect of D for SSS shipwreck recognition.…”

A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps

“…For our applications, EnI is very close to 0, so we defined En min as 0 to guarantee α as a positive value. α min and α max were defined as 0.01 and 0.1, respectively, according to previous experiments [16]. After the β-enhancement operation, T β has a high contrast and can distinctly reflect the target areas, which is useful for accurate target detection using SSS images.…”

“…NSs have been used to solve computer vision problems such as image segmentation, de-noising, and classification [14][15][16]. An NS provides a powerful tool to address the uncertainty problem, being suitable for processing SSS images influenced by complex marine noise.…”