Combined imaging matching method of side scan sonar images with prior position knowledge. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Tao, Weiliang; Liu, Yan
• Is Part Of: IET image processing. Volume 12:Issue 2(2018)
• Page Start: 194
• Page End: 199
• Publication Date: 2018-02-01
• Subjects: sonar -- radar imaging -- image matching -- feature extraction -- affine transforms; combined imaging matching method -- side scan sonar images -- prior position knowledge -- SSS image matching -- feature points -- affine transformations -- feature extraction -- speeded‐up robust features -- geometric correction -- similarity calculation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0172
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Side scan sonar (SSS) image matching plays an important role in underwater applications, such as combining images to form wide range relief images, underwater simultaneous location and mapping, and construction of images of underwater terrain and objects. Because there are differences between the imaging mechanisms of SSS images and optical images, current matching algorithms for optical images do not always work well for SSS images. This study proposes a combined image matching approach for SSS images. First, the images are preprocessed to remove some environmental effects. Second, feature points that are stable for affine transformations are extracted from the SSS matching images based on speeded‐up robust features with prior position knowledge. Third, geometric correction is made by the random sample consensus. After that, a similarity calculation is performed. The proposed approach speeds up the matching process and improves its accuracy by combining feature points matching and similarity calculation. It reduces the mismatching rate and the computation requirement by estimating the location uncertainty with prior knowledge and reducing the searching regions for image matching. Experiments show that the matching algorithm takes less time and has greater reliability and accuracy than traditional algorithms.