Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast

Liu, Jiange; Scott, K. Andrea; Fieguth, Paul

doi:10.1117/1.jrs.13.014505

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…where a is the SIC, and the subscript "t" denotes traditional. This traditional definition has been applied in a variety of applications, such as sea ice charting (e.g., the NIC ice chart), satellite observations (e.g., Strong, 2012;Liu et al, 2019a), sea ice modeling (e.g., Wang et al, 2023), primary productions (e.g., Barber et al, 2015), marine ecosystems (e.g., Wassmann, 2011;Arrigo, 2014), and ship navigation (e.g., Palma et al, 2019).…”

Section: Marginal Ice Zonementioning

confidence: 99%

Multisensor data fusion of operational sea ice observations

Wang,

Dinessen

et al. 2024

Front. Mar. Sci.

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Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.

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Section: Marginal Ice Zonementioning

confidence: 99%

Multisensor data fusion of operational sea ice observations

Wang,

Dinessen

et al. 2024

Front. Mar. Sci.

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“…Experimental results demonstrated that this algorithm effectively enhances the clarity of SAR imagery compared to the Single-Scale Retinex Algorithm, GMM, and Markov Random Field (MRF)-based methods, thereby improving segmentation accuracy. In [30], a multiscale strategy of the curvelet transform was further utilized to extract curve-like features from SAR images, distinguishing the MIZ from open water and consolidated ice areas. Xie et al [31] employed the polarization ratio (PR) between VV and HH in SAR images calculated based on the roughness characteristics of the sea surface scattering and the X-Bragg backscatter model.…”

Section: Other Statistical Approachesmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

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“…A first avenue would take advantage of the distinct kinematic features that characterize the inner ice pack (LKFs, large floes) and the MIZ (mesoscale turbulence, small floes) and involve the development of pattern recognition methods optimized for the MIZs (e.g. [32]). This method has to my knowledge never been developed or implemented.…”

Section: Defining the Marginal Ice Zone And Posing The Problemmentioning

confidence: 99%

Marginal ice zone dynamics: history, definitions and research perspectives

Dumont

2022

Phil. Trans. R. Soc. A.

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Despite enormous scientific and technological progress in numerical weather and climate prediction, sea ice still remains unreliably predicted by models, both in short-term forecasting and climate projection applications. The total ice extent in both hemispheres is tied to the location of the ice edge, and consequently to what happens in the portion of the ice cover immediately adjacent to the open ocean that is called the marginal ice zone (MIZ). There is mounting evidence that processes occurring in the MIZ might play an important role in the polar climate of both hemispheres, yet some key physical processes are still missing in models. As sea ice models developed for climate studies are increasingly used for operational forecasting, the missing physics also impede short-term sea ice prediction skills. This paper is a mini-review that provides a historical perspective on how MIZ research has progressed since the 1970s, with a focus on the fundamental importance of the interactions between sea ice and surface gravity waves on sea ice dynamics. Completeness is not achieved, as the body of literature is huge, scattered and rapidly growing, but the intention is to inform future collaborative research efforts to improve our understanding and predictive capabilities of sea ice dynamics in the MIZ. This article is part of the theme issue ‘Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks’.

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Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast

Cited by 5 publications

References 25 publications

Multisensor data fusion of operational sea ice observations

Multisensor data fusion of operational sea ice observations

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Marginal ice zone dynamics: history, definitions and research perspectives

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