Forward-looking sonar image compression by integrating keypoint clustering and morphological skeleton

Avola, Danilo; Bernardi, Marco; Cinque, Luigi; Foresti, Gian Luca; Pannone, Daniele; Petrioli, Chiara

doi:10.1007/s11042-020-09670-3

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…After the sonar image was rendered in pseudo-color, the location of the pipeline leak can be accurately located. It also improves existing metrics for seabed pipeline leak assessment [115]. Tang proposed an accelerated unsharp mask algorithm based on KAZE feature extraction.…”

Section: Other Technical Applicationsmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

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Section: Other Technical Applicationsmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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“…On the contrary, video sources demand additional computing stages such as video-to-image frame conversion and increased computational cost through video codec computations. In fact, both the image- and video-encoding processes are an issue in deep-sea exploration [ 52 ] due to the limitations of underwater communication. Despite the fact that only lossy video codecs are available in commercial cameras, there are video formats with a greater quality output.…”

Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

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“…Next, they compress each class based on their information content relevant to human interpretation. [24] uses keypoint clustering and morphological skeletonization (MS) to detect and refine areas of interest to adaptively compress the image regions using Brotli compression. [25] uses vector quantization scheme and demonstrate its use in-situ on a UUV.…”

Section: Previous Workmentioning

confidence: 99%

A Learnable Image Compression Scheme for Synthetic Aperture Sonar Imagery

Gerg

Monga

2021

OCEANS 2021: San Diego – Porto

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Synthetic aperture sonar (SAS) is an imaging modality which produces high and constant resolution images of the seafloor. These sonars are often mounted to a unmanned underwater vehicle (UUV) to autonomously collect imagery of a prescribed survey area. While a survey is underway, UUV communications back to the operator are often limited due to the use of a low-bandwidth acoustic communications (ACOMMS) channel. Because of this, high-quality SAS imagery is rarely sent over this link due to the lack of an efficient compression scheme to send such information. Creating an efficient SAS image compression scheme provides at least two operational benefits:(1) image chips beamformed and tagged by onboard processing algorithms can be quickly communicated to operators while a survey is ongoing, and (2) cooperative UUVs can exchange salient image chips among themselves to reconcile position ambiguity and obtain a shared reference frame. In this work we propose a learned image compression scheme for SAS imagery using deep neural networks (DNNs). DNNs have already been applied to the image compression problem but almost exclusively for optical imagery. We highlight some important differences between SAS imagery and optical imagery which prevents the simple application of off-the-shelf (OTS) methods like JPEG and WebP to SAS imagery. We propose an image compression scheme which specifically addresses the domain-specific properties of SAS imagery to obtain useful image compression performance on a real-world SAS dataset. We show that we can reduce the bitrate by up to thirty-five percent while still maintaining the same perceptual image quality as OTS codecs.Index Terms-Synthetic aperture sonar (SAS), image Compression, deep learning, unmanned underwater vehicles, acoustic communications (ACOMMS)

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Forward-looking sonar image compression by integrating keypoint clustering and morphological skeleton

Cited by 5 publications

References 37 publications

Sonar image intelligent processing in seabed pipeline detection: review and application

Sonar image intelligent processing in seabed pipeline detection: review and application

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

A Learnable Image Compression Scheme for Synthetic Aperture Sonar Imagery

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