Automated Iceberg Detection Using High-Resolution X-Band SAR Images

Frost, Anja; Ressel, Rudolf; Lehner, Susanne

doi:10.1080/07038992.2016.1177451

Cited by 24 publications

(35 citation statements)

References 15 publications

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“…Dual-polarization data were actively taken up for operational monitoring [4] and its use has demonstrated an improved discrimination between the open water and sea ice areas compared with single-polarization mode [5]. Within sea ice research, SAR images are used to estimate, e.g., ice drift [6], wave propagation into sea ice, ice concentration [7], and iceberg detection [8], [9]. The long data This work is licensed under a Creative Commons Attribution 3.0 License.…”

Section: Introductionmentioning

confidence: 99%

Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

Singha

Johansson

Hughes

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.

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Section: Introductionmentioning

confidence: 99%

Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

Singha

Johansson

Hughes

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…This threshold is chosen such that the integral of the tail of the SAR signal intensity PDF (f(σ 0 )) is equal to or smaller than a critical Probability of False Alarm (PFA) ( Figure 5). The PDF is obtained from the histogram of the intensity values in a training window surrounding a smaller window in the center (e.g., [14]). In our modified CFAR we determine the background PDF from the iDPolRAD values, denoted as I: from the histogram H(I), obtained from a training window surrounding the pixel under test, we consider only image pixels between an upper and a lower boundary.…”

Section: The Modified Cfar Approachmentioning

confidence: 99%

“…Using the iDPolRAD together with a modified CFAR detector we are here assessing the feasibility of automatic detection of icebergs as small as a single image pixel. The CFAR performance depends on the choice of the probability density function (PDF) of the background clutter and on the threshold for detection (see e.g., [13,14]). We applied the Gamma-, Generalized Gamma-, and Generalized Extreme Value distributions to select a PDF that best matches the iDPolRAD filtered values.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

Soldal

Dierking

Korosov³

et al. 2019

Remote Sensing

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Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8-22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency-polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

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“…The retreat of sea ice in the Arctic is a clear indicator and a visible sign of global warming. The determination of the sea ice thickness [109], composition and extent [110,111] as well as iceberg monitoring [112] provides essential information for operational ice services as well as the modelling of the interaction between the polar oceans and the atmosphere. Information derived from TerraSAR-X data about the sea ice state and in particular the dynamic processes forcing the sea ice breakup are used for ice breaker support [113,114].…”

Section: Cryospherementioning

confidence: 99%

Ten Years of Experience with Scientific TerraSAR-X Data Utilization

Roth

Marschalk

Winkler³

et al. 2018

Remote Sensing

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This paper presents the first comprehensive review on the scientific utilization of earth observation data provided by the German TerraSAR-X mission. It considers the different application fields and technical capabilities to identify the key applications and the preferred technical capabilities of this high-resolution SAR satellite system from a scientific point of view. The TerraSAR-X mission is conducted in a close cooperation with industry. Over the past decade, scientists have gained access to data through a proposal submission and evaluation process. For this review, we have considered 1636 data utilization proposals and analyzed 2850 publications. In general, TerraSAR-X data is used in a wide range of geoscientific research areas comprising anthroposphere, biosphere, cryosphere, geosphere, and hydrosphere. Methodological and technical research is a cross-cutting issue that supports all geoscientific fields. Most of the proposals address research questions concerning the geosphere, whereas the majority of the publications focused on research regarding “methods and techniques”. All geoscientific fields involve systematic observations for the establishment of time series in support of monitoring activities. High-resolution SAR data are mainly used for the determination and investigation of surface movements, where SAR interferometry in its different variants is the predominant technology. However, feature tracking techniques also benefit from the high spatial resolution. Researchers make use of polarimetric SAR capabilities, although they are not a key feature of the TerraSAR-X system. The StripMap mode with three meter spatial resolution is the preferred SAR imaging mode, accounting for 60 percent of all scientific data acquisitions. The Spotlight modes with the highest spatial resolution of less than one meter are requested by only approximately 30 percent of the newly acquired TerraSAR-X data.

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Automated Iceberg Detection Using High-Resolution X-Band SAR Images

Cited by 24 publications

References 15 publications

Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

Ten Years of Experience with Scientific TerraSAR-X Data Utilization

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