Intensity normalization of sidescan sonar imagery

Al-Rawi, Mohammed; Galdrán, Adrián; Yuan, Xin; Eckert, Martina; Martínez-Ortega, José-Fernán; Elmgren, Fredrik; Çürüklü, Baran; Rodrı́guez, Jonathan; Bastos, Joaquim; Pinto, Marc

doi:10.1109/ipta.2016.7820967

Cited by 13 publications

(10 citation statements)

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“…In reviewing the literature, many prior studies in this field [74,75] have noted the importance of using an uniform backscatter intensity mosaic. Radiometric and geometric corrections is an issue of high importance in acoustic imaging, but in SSS data, where there is no auxiliary information about the exact geometry of the ensonification, their elaboration is difficult [73,[76][77][78]. The proposed approach embeds a common technique in satellite and medical image processing for correcting non-uniform intensity, the Homomorphic filtering, which reverses the situation of hidden environment in images.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

et al. 2019

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Bottom trawl footprints are a prominent environmental impact of deep-sea fishery that was revealed through the evolution of underwater remote sensing technologies. Image processing techniques have been widely applied in acoustic remote sensing, but accurate trawl-mark (TM) detection is underdeveloped. The paper presents a new algorithm for the automatic detection and spatial quantification of TMs that is implemented on sidescan sonar (SSS) images of a fishing ground from the Gulf of Patras in the Eastern Mediterranean Sea. This method inspects any structure of the local seafloor in an environmentally adaptive procedure, in order to overcome the predicament of analyzing noisy and complex SSS images of the seafloor. The initial preprocessing stage deals with radiometric inconsistencies. Then, multiplex filters in the spatial domain are performed with multiscale rotated Haar-like features through integral images that locate the TM-like forms and additionally discriminate the textural characteristics of the seafloor. The final TMs are selected according to their geometric and background environment features, and the algorithm successfully produces a set of trawling-ground quantification values that could be established as a baseline measure for the status assessment of a fishing ground.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

et al. 2019

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“…Next, the image is compensated and corrected according to the function obtained by fitting. The representative methods are mixed exponential regression analysis (MIRA) [16] and the method was proposed by Al-Rawi et al [17]. The MIRA method uses an exponential function, such as Equation (2), to fit the gray level change of the side-scan sonar image, and then compensates the image by normalization.…”

Section: Function Fittingmentioning

confidence: 99%

A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

Yang

et al. 2019

Remote Sensing

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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

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“…In our previous works [7,8], we focused on determining underwater geometric features with sonar sensing. Sonar sensors have been widely used in autonomous mobile vehicles, since sound propagates larger distances than electromagnetic waves, sonar imaging outperforms optical imaging in underwater.…”

Section: Introductionmentioning

confidence: 99%

AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Yuan

Martínez-Ortega

Fernández

et al. 2017

Sensors

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In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.

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Intensity normalization of sidescan sonar imagery

Cited by 13 publications

References 10 publications

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

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