Sidescan sonar meets airborne and satellite remote sensing: challenges of a multi-device seafloor classification in extreme shallow water intertidal environments

Capperucci, Ruggero M.; Kubicki, Adam; Höller, Peter; Bartholomä, Alexander

doi:10.1007/s00367-020-00639-7

Cited by 6 publications

(4 citation statements)

References 42 publications

(45 reference statements)

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“…In order to predict seafloor surface characteristics based on their backscatter intensity (i.e., grayscale values) in SSS mosaics, it is necessary to analyze the areal properties and patterns of the grayscale values that are present at a desired location (e.g., see Ref. [25]). Therefore, image patches are extracted from the mosaic at the sampling locations, which establishes a direct link between the seafloor observed in the grab sample and the texture visible in the mosaic and hence enables machine-learning algorithms to learn relationships between the data types.…”

Section: Quantization Level and Size Of The Image Patches For The Cla...mentioning

confidence: 99%

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

Breyer,

Bartholomä,

Pesch

2023

Remote Sensing

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The automatic calculation of sediment maps from hydroacoustic data is of great importance for habitat and sediment mapping as well as monitoring tasks. For this reason, numerous papers have been published that are based on a variety of algorithms and different kinds of input data. However, the current literature lacks comparative studies that investigate the performance of different approaches in depth. Therefore, this study aims to provide recommendations for suitable approaches for the automatic classification of side-scan sonar data that can be applied by agencies and researchers. With random forests, support vector machines, and convolutional neural networks, both traditional machine-learning methods and novel deep learning techniques have been implemented to evaluate their performance regarding the classification of backscatter data from two study sites located in the Sylt Outer Reef in the German Bight. Simple statistical values, textural features, and Weyl coefficients were calculated for different patch sizes as well as levels of quantization and then utilized in the machine-learning algorithms. It is found that large image patches of 32 px size and the combined use of different feature groups lead to the best classification performances. Further, the neural network and support vector machines generated visually more appealing sediment maps than random forests, despite scoring lower overall accuracy. Based on these findings, we recommend classifying side-scan sonar data with image patches of 32 px size and 6-bit quantization either directly in neural networks or with the combined use of multiple feature groups in support vector machines.

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Section: Quantization Level and Size Of The Image Patches For The Cla...mentioning

confidence: 99%

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

Breyer,

Bartholomä,

Pesch

2023

Remote Sensing

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“…For example, echo sounders can only be used above a certain water depth -typically several decimeters. 1 Most airborne bathymetry LiDARs are not suitable for small shallow waters due to long laser pulses and the low spatial resolution caused by the high flight speed and altitude. 2 Also size and weight of many systems require larger carrier platforms which cannot be used economically to survey smaller areas.…”

Section: Introductionmentioning

confidence: 99%

Lightweight dual-wavelength bathymetric LiDAR for accurate seabed mapping

Gangelhoff¹,

Werner²,

Reiterer³

2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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Mapping of small and shallow water bodies is still done primarily in a manual, labour-intensive process. The Fraunhofer Institute for Physical Measurement Techniques IPM developed and evaluated a new compact and lightweight airborne laser-scanning system targeted at topographic and bathymetric surveys which can greatly increase the efficiency and precision. The sensor weighs less than 4 kg and can therefore be operated on many commercially available drones without special permissions according to current regulations. This is a decisive aspect in achieving the necessary operational capability and economic efficiency of such a system. To optimally address the intended use case, our sensor simultaneously emits green and infrared light. This allows for precise separation of water surface and seabed and is especially beneficial for increasing precision in shallow water regions and accurately detecting the shoreline. For sophisticated data analysis, the system records and saves the full signal waveform using high-speed analog-to-digital converters. To ensure a safe operation and reduce the required safety measures, the system is designed as a class 2M laser product. This is achieved through expanding and collimating the outgoing laser beam to approximately 50 mm in diameter and therefore reducing the light intensity. A downward-facing palmer scanner deflects the beam in a near-elliptical shape at approximately 15 • off nadir. The system has been tested and evaluated at a quarry lake in turbid water conditions. The result is a high-resolution ground model which demonstrates the benefits and feasibility of a compact bathymetric scanner and highlights the potential for further research in this field.

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“…However, mapping changes during the construction of underwater protection projects in shallow coastal waters is generally expensive, and frequent surveying can pose a financial challenge for national coastal protection services. The conventional methods used in subtidal zones for underwater monitoring generally comprise either a vessel equipped with a hydroacoustic device [3,19] or video tools and other types of SCUBA-operated devices [20]. Here, we present new possibilities for cost-effective monitoring of underwater construction in the surf zone using unmanned aerial vehicles (UAVs).…”

Section: Introductionmentioning

confidence: 99%

Application of Unmanned Aerial Vehicles and Image Processing Techniques in Monitoring Underwater Coastal Protection Measures

et al. 2022

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A prerequisite for solving issues associated with surf zone variability, which affect human activity in coastal zones, is an accurate estimation of the effects of coastal protection methods. Therefore, performing frequent monitoring activities, especially when applying new nature-friendly coastal defense methods, is a major challenge. In this manuscript, we propose a pipeline for performing low-cost monitoring using RGB images, accessed by an unmanned aerial vehicle (UAV) and a four-level analysis architecture of an underwater object detection methodology. First, several color-based pre-processing activities were applied. Second, contrast-limited adaptive histogram equalization and the Hough transform methodology were used to automatically detect the underwater, circle-shaped elements of a hybrid coastal defense construction. An alternative pipeline was used to detect holes in the circle-shaped elements with an adaptive thresholding method; this pipeline was subsequently applied to the normalized images. Finally, the concatenation of the results from both the methods and the validation processes were performed. The results indicate that our automated monitoring tool works for RGB images captured by a low-cost consumer UAV. The experimental results showed that our pipeline achieved an average error of four pixels in the test set.

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Sidescan sonar meets airborne and satellite remote sensing: challenges of a multi-device seafloor classification in extreme shallow water intertidal environments

Cited by 6 publications

References 42 publications

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

Lightweight dual-wavelength bathymetric LiDAR for accurate seabed mapping

Application of Unmanned Aerial Vehicles and Image Processing Techniques in Monitoring Underwater Coastal Protection Measures

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