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Article ; Online: A comparison of constant false alarm rate object detection algorithms for iceberg identification in L- and C-band SAR imagery of the Labrador Sea

L. Færch / W. Dierking / N. Hughes / A. P. Doulgeris

The Cryosphere, Vol 17, Pp 5335-

2023 Volume 5355

Abstract: In this study, we pursue two objectives: first, we compare six different “constant false alarm rate” (CFAR) algorithms for iceberg detection in SAR images, and second, we investigate the effect of radar frequency by comparing the detection performance at ...

Abstract	In this study, we pursue two objectives: first, we compare six different “constant false alarm rate” (CFAR) algorithms for iceberg detection in SAR images, and second, we investigate the effect of radar frequency by comparing the detection performance at C- and L-band. The SAR images were acquired over the Labrador Sea under melting conditions. In an overlapping optical Sentinel-2 image, 492 icebergs were identified in the area. They were used for an assessment of the algorithms' capabilities to accurately detect them in the SAR images and for the determination of the number of false alarms and missed detections. By testing the detectors at varying probability of false alarm (PFA) levels, the optimum PFA for each detector was found. Additionally, we considered the effect of iceberg sizes in relation to image resolution. The results showed that the overall highest accuracy was achieved by applying a log-normal CFAR detector to the L-band image ( F score of 70.4 %), however, only for a narrow range of PFA values. Three of the tested detectors provided high F scores above 60 % over a wider range of PFA values both at L- and C-band. Low F scores were mainly caused by missed detections of small-sized ( < 60 m) and medium-sized (60–120 m) icebergs, with approximately 20 %–40 % of the medium icebergs and 85 %–90 % of small icebergs being missed by all detectors. The iDPolRAD detector, which is sensitive to volume scattering, is less suitable under melting conditions.
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	290
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture

W. Guo / P. Itkin / S. Singha / A. P. Doulgeris / M. Johansson / G. Spreen

The Cryosphere, Vol 17, Pp 1279-

2023 Volume 1297

Abstract: We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary ... ...

Abstract	We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar ...
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	290
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity

W. Guo / P. Itkin / J. Lohse / M. Johansson / A. P. Doulgeris

The Cryosphere, Vol 16, Pp 237-

2022 Volume 257

Abstract: Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features ... ...

Abstract	Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. Also, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and fine quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from the two C-band sensors. We use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers per-ice-type incident angle (IA) dependency of backscatter intensity. This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions during the Norwegian young sea ICE (N-ICE2015) expedition. Manually drawn polygons of different ice types for S1 EW, RS2 SCWA and RS2 FQ data are used to retrain the classifier. Different training sets yield similar classification results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads” class). This is caused by different noise floor configurations of S1 and RS2 data, which interact differently with leads, necessitating dataset-specific retraining for this class. SAR scenes are then classified based on the classifier retrained for each dataset, with the classification scheme altered to separate level from deformed ice to enable direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities between ice types with higher backscatter intensities. This study provides reference to future ...
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	290
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

A. S. Fors / D. V. Divine / A. P. Doulgeris / A. H. H. Renner / S. Gerland

The Cryosphere, Vol 11, Iss 2, Pp 755-

2017 Volume 771

Abstract: In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to ... ...

Abstract	In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6. 2 m s −1 ), with a Spearman's correlation coefficient of 0. 46. At low wind speeds (0. 6 m s −1 ), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of −0. 53. To further investigate these relations, regression fits were made both for the intermediate ( R 2 fit = 0. 21) and low ( R 2 fit = 0. 26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4 % from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51 × 51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above ∼ 40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future.
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	333
Language	English
Publishing date	2017-03-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

A. S. Fors / C. Brekke / A. P. Doulgeris / T. Eltoft / A. H. H. Renner / S. Gerland

The Cryosphere, Vol 10, Iss 1, Pp 401-

2016 Volume 415

Abstract: In this study, we investigate the potential of sea ice segmentation by C- and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five high-resolution satellite SAR scenes were recorded in the Fram Strait covering ... ...

Abstract	In this study, we investigate the potential of sea ice segmentation by C- and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five high-resolution satellite SAR scenes were recorded in the Fram Strait covering iceberg-fast first-year and old sea ice during a week with air temperatures varying around 0 °C. Sea ice thickness, surface roughness and aerial photographs were collected during a helicopter flight at the site. Six polarimetric SAR features were extracted for each of the scenes. The ability of the individual SAR features to discriminate between sea ice types and their temporal consistency were examined. All SAR features were found to add value to sea ice type discrimination. Relative kurtosis, geometric brightness, cross-polarisation ratio and co-polarisation correlation angle were found to be temporally consistent in the investigated period, while co-polarisation ratio and co-polarisation correlation magnitude were found to be temporally inconsistent. An automatic feature-based segmentation algorithm was tested both for a full SAR feature set and for a reduced SAR feature set limited to temporally consistent features. In C band, the algorithm produced a good late-summer sea ice segmentation, separating the scenes into segments that could be associated with different sea ice types in the next step. The X-band performance was slightly poorer. Excluding temporally inconsistent SAR features improved the segmentation in one of the X-band scenes.
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	290 ; 550
Language	English
Publishing date	2016-02-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Late summer sea ice segmentation with multi-polarisation SAR features in C- and X-band

A. S. Fors / C. Brekke / A. P. Doulgeris / T. Eltoft / A. H. H. Renner / S. Gerland

The Cryosphere Discussions, Vol 9, Iss 5, Pp 4539-

2015 Volume 4581

Abstract: In this study we investigate the potential of sea ice segmentation by C- and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five high-resolution satellite SAR scenes were recorded in the Fram Strait covering iceberg- ...

Abstract	In this study we investigate the potential of sea ice segmentation by C- and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five high-resolution satellite SAR scenes were recorded in the Fram Strait covering iceberg-fast first-year and old sea ice during a week with air temperatures varying around zero degrees Celsius. In situ data consisting of sea ice thickness, surface roughness and aerial photographs were collected during a helicopter flight at the site. Six polarimetric SAR features were extracted for each of the scenes. The ability of the individual SAR features to discriminate between sea ice types and their temporally consistency were examined. All SAR features were found to add value to sea ice type discrimination. Relative kurtosis, geometric brightness, cross-polarisation ratio and co-polarisation correlation angle were found to be temporally consistent in the investigated period, while co-polarisation ratio and co-polarisation correlation magnitude were found to be temporally inconsistent. An automatic feature-based segmentation algorithm was tested both for a full SAR feature set, and for a reduced SAR feature set limited to temporally consistent features. In general, the algorithm produces a good late summer sea ice segmentation. Excluding temporally inconsistent SAR features improved the segmentation at air temperatures above zero degrees Celcius.
Keywords	Science ; Q ; Geology ; QE1-996.5 ; Petrology ; QE420-499 ; Dynamic and structural geology ; QE500-639.5
Subject code	550
Language	English
Publishing date	2015-09-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts

M.-A. N. Moen / A. P. Doulgeris / S. N. Anfinsen / A. H. H. Renner / N. Hughes / S. Gerland / T. Eltoft

The Cryosphere Discussions, Vol 7, Iss 3, Pp 2595-

2013 Volume 2634

Abstract: In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to ... ...

Abstract	In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in-situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.
Keywords	Environmental sciences ; GE1-350 ; Geography. Anthropology. Recreation ; G ; DOAJ:Environmental Sciences ; DOAJ:Earth and Environmental Sciences ; Meteorology. Climatology ; QC851-999 ; Physics ; QC1-999 ; Science ; Q ; DOAJ:Meteorology and Climatology ; Geology ; QE1-996.5 ; Petrology ; QE420-499 ; Dynamic and structural geology ; QE500-639.5
Subject code	290
Language	English
Publishing date	2013-06-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts

M. -A. N. Moen / A. P. Doulgeris / S. N. Anfinsen / A. H. H. Renner / N. Hughes / S. Gerland / T. Eltoft

The Cryosphere, Vol 7, Iss 6, Pp 1693-

2013 Volume 1705

Abstract	In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Utilizing polarimetric information in sea ice charting will increase the efficiency and exactness of the maps. The number of classes used in the segmentation has shown to be of significant importance. Thus, studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.
Keywords	Environmental sciences ; GE1-350 ; Geology ; QE1-996.5
Subject code	290
Language	English
Publishing date	2013-11-01T00:00:00Z
Publisher	Copernicus Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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