Real-time Model-based Image Color Correction for Underwater Robots

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

The Underwater Photic Environment of Cape Maclear, Lake Malawi: Comparison Between Rock- and Sand-Bottom Habitats and Implications for Cichlid Fish Vision

Lake Malawi boasts the highest diversity of freshwater fishes in the world. Nearshore sites are categorized according to their bottom substrate, rock or sand, and these habitats host divergent assemblages of cichlid fishes. Sexual selection driven by mate choice in cichlids led to spectacular diversification in male nuptial coloration. This suggests that the spectral radiance contrast of fish, the main determinant of visibility under water, plays a crucial role in cichlid visual communication. This study provides the first detailed description of underwater irradiance, radiance and beam attenuation at selected sites representing two major habitats in Lake Malawi. These quantities are essential for estimating radiance contrast and, thus, the constraints imposed on fish body coloration. Irradiance spectra in the sand habitat were shifted to longer wavelengths compared with those in the rock habitat. Beam attenuation in the sand habitat was higher than in the rock habitat. The effects of water depth, bottom depth and proximity to the lake bottom on radiometric quantities are discussed. The radiance contrast of targets exhibiting diffused and spectrally uniform reflectance depended on habitat type in deep water but not in shallow water. In deep water, radiance contrast of such targets was maximal at long wavelengths in the sand habitat and at short wavelengths in the rock habitat. Thus, to achieve conspicuousness, color patterns of rock-and sand-dwelling cichlids would be restricted to short and long wavelengths, respectively. This study provides a useful platform for the examination of cichlid visual communication

Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration

Author Posting. © Optical Society of America, 2014. This article is posted here by permission of Optical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Optical Society of America A: Optics, Image Science, and Vision 31 (2014): 312-321, doi:10.1364/JOSAA.31.000312.Commercial off-the-shelf digital cameras are inexpensive and easy-to-use instruments that can be used for quantitative scientific data acquisition if images are captured in raw format and processed so that they maintain a linear relationship with scene radiance. Here we describe the image-processing steps required for consistent data acquisition with color cameras. In addition, we present a method for scene-specific color calibration that increases the accuracy of color capture when a scene contains colors that are not well represented in the gamut of a standard color-calibration target. We demonstrate applications of the proposed methodology in the fields of biomedical engineering, artwork photography, perception science, marine biology, and underwater imaging.T. Treibitz is an Awardee of the Weizmann Institute of Science—National Postdoctoral Award Program for Advancing Women in Science and was supported by NSF grant ATM-0941760. D. Akkaynak, J. Allen, and R. Hanlon were supported by NSF grant 1129897 and ONR grants N0001406-1-0202 and N00014-10-1-0989 and U. Demirci by grants R01AI093282, R01AI081534, and NIH U54EB15408. J. Allen is grateful for support from a National Defense Science and Engineering Graduate Fellowship

A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats

We developed a novel integrated technology for diver-operated surveying of shallow marine ecosystems. The HyperDiver system captures rich multifaceted data in each transect: hyperspectral and color imagery, topographic profiles, incident irradiance and water chemistry at a rate of 15-30 m(2) per minute. From surveys in a coral reef following standard diver protocols, we show how the rich optical detail can be leveraged to generate photopigment abundance and benthic composition maps. We applied machine learning techniques, with a minor annotation effort (<2% of pixels), to automatically generate cm-scale benthic habitat maps of high taxonomic resolution and accuracy (93-97%). The ability to efficiently map benthic composition, photopigment densities and rugosity at reef scales is a compelling contribution to modernize reef monitoring. Seafloor-level hyperspectral images can be used for automated mapping, avoiding operator bias in the analysis and deliver the degree of detail necessary for standardized environmental monitoring. The technique can deliver fast, objective and economic reef survey results, making it a valuable tool for coastal managers and reef ecologists. Underwater hyperspectral surveying shares the vantage point of the high spatial and taxonomic resolution restricted to field surveys, with analytical techniques of remote sensing and provides targeted validation for aerial monitoring

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process

Hyperspectral benthic mapping from underwater robotic platforms

We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

Funding This research received no external funding. Acknowledgments We are also grateful to Manas Sarkar, ITC, HKPU for providing cotton samples with varied textures and Dystar, Hong Kong, for generously providing us with dye samples. We are thankful to for the experimental support from new fiber science and IoT Lab, OUTR sponsored by TEQIP-3 seed money and MODROB (/9-34/RIFDMO DPOLICY-1/2018-19).Peer reviewedPublisher PD

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure