Underwater Hyperspectral Imaging Technology and Its Applications for Detecting and Mapping the Seafloor: A Review

Liu, Bohan; Liu, Zhaojun; Men, Shaojie; Li, Yongfu; Ding, Zhongjun; He, Jiahao; Zhao, Zhigang

doi:10.3390/s20174962

Cited by 50 publications

(33 citation statements)

References 95 publications

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“…8. F1-score: It is the weighted mean of sensitivity and specificity and can be given by Equation (7).…”

Section: Accuracymentioning

confidence: 99%

“…In [6], they demonstrated the framework of using radar to monitor coral bleaching, which got a significant drawback of too much equipment to be utilized on the ocean surface and very expensive. In [7], they demonstrated the use of an airborne hyperspectral sensor to classify bleached corals. The airborne hyper-spectral sensor ranked only twenty-four points correctly out of thirty points and has a classification accuracy of 80%.…”

Section: Introductionmentioning

confidence: 99%

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Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Jamil

Rahman

Haider

2021

BDCC

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Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.

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“…8. F1-score: It is the weighted mean of sensitivity and specificity and can be given by Equation (7).…”

Section: Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Jamil

Rahman

Haider

2021

BDCC

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“…This technique combines the spatial structures and spectral characteristics of the targets simultaneously, which realizes the synchronous detection of spatial dimension and spectral dimension, and provides the possibility of classification and identification of the target. Currently, spectral imaging systems based on shipborne, airborne, and spaceborne platforms have been capable of detecting objects dozens of meters underwater [66]. However, the above spectral imaging systems cannot achieve the perception of deep-sea targets due to the strong attenuation caused by water.…”

Section: E Spectral Imagingmentioning

confidence: 99%

Underwater Optical Imaging: Key Technologies and Applications Review

et al. 2021

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The challenges associated with acquiring the clear images of objects in underwater environment are difficult to overcome due to the absorptive and scattering nature of seawater. Recently, the research community has focused on mitigating these effects. The recent developments in image enhancement algorithms and strategies of signal light enhancement have brought improvement in some application areas. In this work, we review the six most common methods based on signal light enhancement. We present the individual working mechanisms, latest representative advances, and suitable application conditions. Moreover, we also present a detailed comparison of these techniques. In each technique, we present their applicable environments and conditions according to the following indicators: operating distance (from 2 attenuation lengths (AL) to 13.5 AL), resolution (from centimeter to millimeter), and field of view (FOV). By summarizing and analyzing the existing problems that restrict the underwater optical imaging techniques, the future development trends are prospected. INDEX TERMSUnderwater optical imaging, Underwater imaging model, Signal light enhancement, Optical properties of seawater

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“…Several spectral imaging techniques are being adopted to perform a wide range of underwater detection, including starring type, snapshot, and push-broom imaging methods [23]. To maximize the performance and minimize the mechanical complexity of spectral imaging for underwater application, this paper presents the design and development of a stare-type USI system based on LCTF.…”

Section: Introductionmentioning

confidence: 99%

Underwater Spectral Imaging System Based on Liquid Crystal Tunable Filter

Song

Mehdi

et al. 2021

JMSE

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In the past decade, underwater spectral imaging (USI) has shown great potential in underwater exploration for its high spectral and spatial resolution. This proposal presents a stare-type USI system combined with the liquid crystal tunable filter (LCTF) spectral splitting device. Considering the working features of LCTF and the theoretical model of USI, the core structure containing “imaging lens-LCTF-imaging sensor” is designed and developed. The system is compact, and the optical geometry is constructed minimally. The spectral calibration test analysis proved that the spectral response range of the system covers a full band of 400 nm to 700 nm with the highest spectral resolution between 6.7 nm and 18.5 nm. The experiments show that the system can quickly collect high-quality spectral image data by switching between different spectral bands arbitrarily. The designed prototype provides a feasible and reliable spectral imaging solution for in situ underwater targets observation with high spectrum collecting efficiency.

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Underwater Hyperspectral Imaging Technology and Its Applications for Detecting and Mapping the Seafloor: A Review

Cited by 50 publications

References 95 publications

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Underwater Optical Imaging: Key Technologies and Applications Review

Underwater Spectral Imaging System Based on Liquid Crystal Tunable Filter

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