In situ Measurement of Reflectance and Fluorescence Spectra to Support Hyperspectral Remote Sensing and Marine Biology Research

Mazel, Charles H.

doi:10.1109/oceans.2006.307001

Cited by 19 publications

(8 citation statements)

References 12 publications

(13 reference statements)

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“…This method is realized by simple pixel value redistribution without relying on the underwater optical imaging model. The main contributions of this paper can be summarized as follows: (1) Regarding the problem that the saliency weight map in the fusion algorithm cannot effectively distinguish the saliency target, we updated the saliency weight map by combining contrast and spatial cues to highlight the obvious target in the processed image. (2) To further enhance the brightness of the fused image, after stretching the histogram of the RGB channel, the pixel values in the L channel of the Lab model that range between 0.1% and 99.9% are stretched to the range [0, 100].…”

Section: Discussionmentioning

confidence: 99%

“…Underwater vision has played an important role in different fields of science such as marine biology research [1], inspection of underwater man-made objects [2], and control of underwater vehicles [3]. Underwater images suffer from color cast and poor visibility resulting from absorption and scattering effects in the optical propagation process [4,5], which makes the enhancement and restoration of underwater images a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Song

Wang

2021

Mathematics

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Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Song

Wang

2021

Mathematics

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“…Field spectroscopy serves as a fundamental stage for primary research into remote sensing practice and operational applications, such as for calibration and validation [1][2][3]. Spectroscopy protocols practiced worldwide record metadata relating to the instrument properties, illumination and viewing angles, reference standards and general project information on an ad hoc basis [4][5][6] in a variety of formats including logsheets, and occasionally, databases.…”

Section: The Importance Of Field Metadatamentioning

confidence: 99%

Critical Metadata for Spectroscopy Field Campaigns

et al. 2014

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Abstract:A field spectroscopy metadata standard is defined as those data elements that explicitly document the spectroscopy dataset and field protocols, sampling strategies, instrument properties and environmental and logistical variables. Standards for field spectroscopy metadata affect the quality, completeness, reliability, and usability of datasets created in situ. Currently there is no standardized methodology for documentation of in situ spectroscopy data or metadata. This paper presents results of an international experiment comprising a web-based survey and expert panel evaluation that investigated critical metadata in field spectroscopy. The survey participants were a diverse group of scientists experienced in gathering spectroscopy data across a wide range of disciplines. Overall, respondents were in agreement about a core metadataset for generic campaign metadata, allowing for a prioritization of critical metadata elements to be proposed including those relating to viewing geometry, location, general target and sampling properties, illumination, instrument properties, reference standards, calibration, hyperspectral signal properties, atmospheric conditions, and general project details. Consensus was greatest among individual expert groups in specific application domains. The results allow the identification of a core set of metadata fields that enforce long term data storage and serve as a foundation for a metadata standard. This paper is part one in a series about the core elements of a robust and flexible field spectroscopy metadata standard.

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“…The ocean, which occupies 71% of the earth's surface area, is one of the hottest areas of exploration at this stage. Recently, underwater images have become the most effective tools to explore this treasure trove of resources and have emerged in a wide spectrum of aquatic applications, such as deep ocean exploration, inspection of underwater infrastructures [1] and cables [2], sea life monitoring [3], archeology [4], and control of underwater robotics [5].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement

Zhou,

Yan

2020

Preprint

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Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion. These characteristics can significantly interfere with the visibility of underwater images and the result of visual tasks, such as segmentation and tracking. To address this problem, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images to get realistic results. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed.Upon the synthetic dataset, a novel enhancement framework, which introduces a domain adaptive mechanism as well as a physics model constraint feedback control, is trained to enhance the underwater scenes. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component we proposed.

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In situ Measurement of Reflectance and Fluorescence Spectra to Support Hyperspectral Remote Sensing and Marine Biology Research

Abstract: A new diver-operated instrument for in situ spectral measurement of reflectance, fluorescence, and ambient light has been developed. These measurements are needed to support interpretation of hyperspectral remotely sensed imagery and for fundamental studies of marine biology.

Cited by 19 publications

References 12 publications

Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Critical Metadata for Spectroscopy Field Campaigns

Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement

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