EISCAM — Digital image acquisition and processing for sea ice parameters from ships

Weissling, Blake; Ackley, Stephen F.; Wagner, P.; Xie, Hongjie

doi:10.1016/j.coldregions.2009.01.001

Cited by 42 publications

(31 citation statements)

References 18 publications

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“…Most of the high-resolution sea ice photos were analyzed through pixel-based methods [15,16,30]. This method is based on pixel brightness values or spectral values, ignoring spatial autocorrelation, and generates 'salt-and-pepper' noise in the classification [31,32].…”

Section: Object-based Image Segmentationmentioning

confidence: 99%

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sha

Miao

et al. 2020

Data

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Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

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Section: Object-based Image Segmentationmentioning

confidence: 99%

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sha

Miao

et al. 2020

Data

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“…It would be highly desirable to update such valuable data annually. Additional systematic data collection devices have been developed, for example Evaluative Imagery Support Camera (EISCam; Weissling et al 2009) that could augment ship-based observations. Another suggestion was that increased validation and ground truth data could be collected using autonomous platforms from stations on sea ice for validation (time series) and airborne data to fill the gaps of observational scales (between transects and satellites). Another recommendation suggested tourist and base resupply vessels and icebreakers could be used as satellite data validation platforms, typically during the spring and summer between November and February. Survey respondents noted that the utility of in situ measurements could be improved with more complete and discoverable metadata, as this information is difficult to find when trying to match in situ observations with coincident satellite data.…”

Section: Sea Icementioning

confidence: 99%

“…This would benefit the science community because more frequent observations and visual confirmation of prevailing sea ice conditions would then be available. Further to this, human observers could also be supplemented by a wider deployment of IceCam/EISCam technology allowing quantitative image analysis techniques to be used (Hall et al 2002, Weissling et al 2009). Continuity of existing sensors and restoration of previous sensors.…”

Section: Sea Icementioning

confidence: 99%

“…Regarding sea ice conditions, additional data collection simultaneous to improved satellite collections should include in situ measurements of snow cover, sea ice thickness, pressure ridging, sea ice draft data and sea winds (e.g. following ASPeCt, Worby et al 2008, Weissling et al 2009). Though the community is aware of this need, it is important to implement greater co-ordination in collection of these data, between all groups who are physically travelling to these regions.…”

Section: Importance Of Coincident Datamentioning

confidence: 99%

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Community review of Southern Ocean satellite data needs

et al. 2016

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This review represents the Southern Ocean community's satellite data needs for the coming decade. Developed through widespread engagement and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea ice properties, sea surface temperature, sea surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increases in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/ products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.

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“…These passive systems complement the active remote sensors such as radar and lidar. Oceanographic applications of remote sensing include direct measurements of whitecap coverage (Callaghan and White 2009) and sea ice concentration (Weissling et al 2009). In addition, underlying properties such as wave dissipation, bathymetry, and currents can sometimes be inferred from remote measurements (Sutherland and Melville 2013;Stockdon and Holman 2000;Chickadel et al 2003).…”

Section: Introductionmentioning

confidence: 99%

A Horizon-Tracking Method for Shipboard Video Stabilization and Rectification

Schwendeman

Thomson

2015

Journal of Atmospheric and Oceanic Technology

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An algorithm is presented for the stabilization and rectification of digital video from floating platforms. The method relies on a horizon-tracking technique that was tested under a variety of lighting and sea-state conditions for 48 h of video data over 12 days during a research cruise in the North Pacific Ocean. In this dataset, the horizon was correctly labeled in 92% of the frames in which it was present. The idealized camera model assumes pure pitch-and-roll motion, a flat sea surface, and an unobstructed horizon line. Pitch and roll are defined along the camera look direction rather than in traditional ship coordinates, such that the method can be used for any heading relative to the ship. The uncertainty in pitch and roll is estimated from the uncertainties of the horizon-finding method. These errors are found to be of the order 0.68 in roll and 0.38 in pitch. Errors in rectification are shown to be dominated by the uncertainty in camera height, which may change with the heave motion of a floating platform. The propagation of these errors is demonstrated for the breaking-wave distribution L(c). A toolbox for implementation of this method in MATLAB is shared via the MATLAB File Exchange.

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EISCAM — Digital image acquisition and processing for sea ice parameters from ships

Cited by 42 publications

References 18 publications

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Community review of Southern Ocean satellite data needs

A Horizon-Tracking Method for Shipboard Video Stabilization and Rectification

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