Sidescan Sonar Image Interpretation With Neural Networks

Bourgeois, Brian S.; Walker, Charles L.

doi:10.1109/oceans.1991.606553

Cited by 14 publications

(5 citation statements)

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“…In remote sensing, for hyperspectral imagery, (Liu et al, 2010) proposed an approach based on SOM and fuzzy membership for decomposition of mixed pixels. Several authors have successfully applied different approaches of ANN to the problem of seafloor classification (Muller et al, 1997;Stewart et al, 1994;Bourgeois and Walker, 1919;Maillard et al, 1992;Vink et al, 2000). Similarly, the use of fuzzy ART algorithm for the segmentation of acoustic image is implemented by (Vink et al, 2000).…”

Section: Related Workmentioning

confidence: 99%

Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Islam

Zerr

2017

Ocean Engineering

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International audienceIn this paper we introduce a new unsupervised segmentation algorithm for textured sonar images. A Dynamic Self-Organizing Maps (DSOM) algorithm capable of incremental learning has been developed to automatically cluster the input data into relevant classes of seabed. DSOM algorithm is an extension of classical Self Organizing Maps (SOM) algorithm combined with Adaptive Resonance Theory (ART) technique. The proposed approach is based on growing map size during learning processes. Starting with a minimal number of neurons, the map size increases dynamically and the growth is controlled by the vigilance threshold of the ART network. To assess the consistency of the proposed approach, the DSOM algorithm is first tested on simulated data sets and then applied on real sidescan sonar images. The results obtained using the proposed approach demonstrate its capability to successfully cluster sonar images into their relevant seabed classes, very close to those resulting from human expert interpretation

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Section: Related Workmentioning

confidence: 99%

Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Islam

Zerr

2017

Ocean Engineering

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“…While neural networks have been previously applied to backscatter mosaics in the field of marine habitat mapping [18,19], these were not convolutional networks optimized for image analysis. Convolutional neural networks operate by convolving an input image with different kernel functions, thus generating a large number of filtered images that differ due to the different weights of the kernels.…”

Section: Introductionmentioning

confidence: 99%

Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network

Feldens

Darr

Feldens³

et al. 2019

Geosciences

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Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1-5, more than 5) over 10,000 m 2 areas, with the best performing model reaching an agreement with the human interpretation of 90%.

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“…Malik and Mayer [44] noted that video images depict the TMs with significantly less accuracy than SSS, while Tang et al [45] analyzed the trawling pattern in video images with a neural classifier. Despite the fact that there has recently been strong interest in deep learning methods for geological seafloor mapping [46,47] there are no studies using machine learning methods for TM recognition in SSS images. To the best of our knowledge, up to today there is no TM detection algorithm in the literature applied to a large SSS mosaic that could detect and quantify TMs for their environmental impact monitoring use.…”

Section: Introductionmentioning

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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Sidescan Sonar Image Interpretation With Neural Networks

Cited by 14 publications

References 10 publications

Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network

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

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