SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics

Yu, Qihao; Clausi, David A.

doi:10.1109/tgrs.2007.908876

Cited by 104 publications

(61 citation statements)

References 39 publications

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“…Alternative unsupervised and supervised classification methods may provide better results [52]. Methods that have been applied successfully to the clustering of SAR pixels include Kohonen Self Organizing Maps [53], Mean-Shift [54], and iterative region growing [55]. Notably, reference [56] studies variations in statistical feature values between land cover classes derived from an ERS SAR image of an overlapping area.…”

Section: Methods Improvementmentioning

confidence: 99%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial-and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a "mega-scale" beach nourishment project in the Netherlands. Natural processes, like waves, wind, and tides, gradually distribute the highly concentrated sand to adjacent beaches. Currently, various in-situ techniques are used to monitor the Sand Motor on a monthly basis. Meanwhile, the TerraSAR-X satellite collects two high-resolution (3 × 3 m), cloud-penetrating SAR images every 11 days. This study investigates whether shorelines detected in TerraSAR-X imagery are accurate enough to monitor the shoreline dynamics of a project like the Sand Motor. The study proposes and implements a semi-automated workflow to extract shorelines from all 182 available TerraSAR-X images acquired between 2011 and 2014. The shorelines are validated using bi-monthly RTK-GPS topographic surveys and nearby wave and tide measurements. A valid shoreline could be extracted from 54% of the images. The horizontal accuracy of these shorelines is approximately 50 m, which is sufficient to assess the larger scale shoreline dynamics of the Sand Motor. The accuracy is affected strongly by sea state and partly by acquisition geometry. We conclude that using frequent, high-resolution TerraSAR-X imagery is a valid option for assessing coastal dynamics on the order of tens of meters at approximately monthly intervals.

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Section: Methods Improvementmentioning

confidence: 99%

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

et al. 2017

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“…Some studies on using single-band SAR for the SIC estimation have, however, been conducted [9][10][11][12][13][14][15][16][17]. Automated sea ice classification schemes-which implicitly include SIC or an open-water class-based on single-band SAR texture have been proposed [18][19][20][21][22][23][24]. These methods use multiple techniques such as the gray-level co-occurrence texture features [25], Markov random fields [26], and Gabor filters [18,27] to classify the SAR imagery.…”

Section: Sea Ice Concentrationmentioning

confidence: 99%

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

et al. 2017

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Abstract:We estimate two essential sea ice parameters-namely, sea ice concentration (SIC) and sea ice thickness (SIT)-for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT.

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“…The methods lend themselves to seek greater accuracy by further analysis, building on thresholding and region-growing techniques by, for example, Yu and Clausi (2007);Matgen et al (2011);Galland et al (2009) and Silveira and Heleno (2009). Other successful segmentation methods for SAR images involving texture and shape (van der Werff and van der Meer, 2007), active contours (Ben Ayed et al, 2005;Chakraborty et al, 2009;Fu et al, 2008) and multi-objective algorithms (Collins and Kopp, 2008) may be suitable.…”

Section: Use and Limitations Of The Gm Data For Flood Mappingmentioning

confidence: 99%

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

O’Grady

Leblanc

Gillieson

2011

Hydrol. Earth Syst. Sci.

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Abstract. Envisat ASAR Global Monitoring Mode (GM) data are used to produce maps of the extent of the flooding in Pakistan which are made available to the rapid response effort within 24 h of acquisition. The high temporal frequency and independence of the data from cloud-free skies makes GM data a viable tool for mapping flood waters during those periods where optical satellite data are unavailable, which may be crucial to rapid response disaster planning, where thousands of lives are affected. Image differencing techniques are used, with pre-flood baseline image backscatter values being deducted from target values to eliminate regions with a permanent flood-like radar response due to volume scattering and attenuation, and to highlight the low response caused by specular reflection by open flood water. The effect of local incidence angle on the received signal is mitigated by ensuring that the deducted image is acquired from the same orbit track as the target image. Poor separability of the water class with land in areas beyond the river channels is tackled using a region-growing algorithm which seeks threshold-conformance from seed pixels at the center of the river channels. The resultant mapped extents are tested against MODIS SWIR data where available, with encouraging results.

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SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics

Cited by 104 publications

References 39 publications

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

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