Iceberg Detection Research Articles

Iceberg Detection in Satellite Images using Deep Learning Techniques

Iceberg detection is found to be more critical in the previous researchers. High quality satellite monitoring of dangerous ice formations is critical to navigation safety and economic activity in the regions. The satellite images play a crucial role in the identification of the icebergs. In this manuscript, a convolutional neural network (CNN) model is proposed for the iceberg detection from the satellite images. It is based on the satellite dataset for target classification and target identification. The iceberg detection is based on the statistical criteria for finding the satellite images. This model is used to identify automatically whether it is remote sensed target is iceberg or not. Sometimes the iceberg is wrongly classified as ship. This model is done to make accurate about the changes in the detection.

A Computational Framework for Iceberg and Ship Discrimination: Case Study on Kaggle Competition

Iceberg and ship identification in satellite synthetic aperture radar (SAR) data plays an important role in offering an operational iceberg surveillance program. Here, the identification aims to detect ocean SAR targets and then categorize these targets into iceberg, ship, or unknown. Although the adaptive threshold techniques have achieved promising results on the ship and iceberg detection in SAR images, the discrimination between these two target classes is still very challenging for operational scenarios. This study presents a computational framework for iceberg and ship discrimination based on an ensemble of various deep learning and machine learning algorithms. On one hand, latest deep neural networks - namely, DenseNet and ResNet - are deployed in this study for end-to-end feature exaction and image classification directly on original SAR images. On the other hand, handcrafted features are extracted on de-speckled SAR images, followed by classification using advanced machine learning algorithms - namely, XGBoost and LightGBM. The outcomes from both sides are then combined through min-max median stacking approach to classify the given SAR images into iceberg and ship categories. The proposed framework has recently been deployed as the key kernel for the “Statoil/C-CORE Iceberg Classifier Challenge” organized by Kaggle. The performance is promising as our final scores were ranked 26 and 39 out of 3343 teams on public and private leaderboards, respectively. We hope that by sharing the solutions, we can further promote research interests in the field of iceberg and ship identification.

An adaptive machine learning approach to improve automatic iceberg detection from SAR images

Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003–2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near-coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 ± 0.4% and 3.3 ± 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km2 are detected with average classification accuracy of 97.5 ± 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.

The life cycle of small- to medium-sized icebergs in the Amundsen Sea Embayment

An object-based method for automatic iceberg detection has been applied to Advanced Synthetic Aperture Radar images in the Amundsen Sea Embayment (ASE), Antarctica. The images were acquired between 1 January 2006 and 8 April 2012 under varying meteorological, oceanographic and sea-ice conditions. During this time period, the icebergs were counted (average 1370 ± 50) and their surface area was estimated (average 1537.5 km2). The average surface area was about 2.5 times larger than the annual calved area (620 km2), indicating that the average iceberg age in the ASE is about 2.5 years, which was confirmed by observed residence times based on drift tracks. Most of the ASE icebergs were less than 1500 m long, and almost 90% of them were smaller than 2 km2. The proportion of small- and medium-sized icebergs (84.4%) was significantly higher than in the open ocean, where large icebergs (>10 km2) account for nearly the whole iceberg surface area. The opposite was true for the freshly calved icebergs in the ASE. The data indicate that the creation of icebergs in the ASE is dominated by steady small- to medium-scale calving from ice shelves fringing the embayment. In addition, rare calving events of giant icebergs occur on a decadal timescale. There is also some import of icebergs from the Bellingshausen Sea further east along the coast, in particular after large calving events there.

Semi-automated open water iceberg detection from Landsat applied to Disko Bay, West Greenland.

Changes in Greenland’s marine-terminating outlet glaciers have led to changes in the flux of icebergs into Greenland’s coastal waters, yet icebergs remain a relatively understudied component of the ice-ocean system. We developed a simple iceberg delineation algorithm for Landsat imagery. A machine learning-based cloud mask incorporated into the algorithm enables us to extract iceberg size distributions from open water even in partially cloudy scenes. We applied the algorithm to the Landsat archive covering Disko Bay, West Greenland, to derive a time series of iceberg size distributions from 2000–02 and 2013–15. The time series captures a change in iceberg size distributions, which we interpret as a result of changes in the calving regime of the parent glacier, Sermeq Kujalleq (Jakobshavn Isbræ). The change in calving style associated with the disintegration and disappearance of Sermeq Kujalleq’s floating ice tongue resulted in the production of more small icebergs. The increased number of small icebergs resulted in increasingly negative power law slopes fit to iceberg size distributions in Disko Bay, suggesting that iceberg size distribution time series provide useful insights into changes in calving dynamics.

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

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.

Satellite Monitoring of Icebergs in the Arctic Seas

The study present the results of the satellite monitoring of Arctic icebergs formed from the outlet glaciers of the Severnaya Zemlya, Novaya Zemlya, and Franz Josef Land archipelagos during the autumn-winter and spring periods. The algorithms for iceberg detection in satellite images under different meteorological and ice conditions are described.

Use of satellite data for detecting icebergs and evaluating the iceberg threats

Te methods of satellite monitoring of dangerous ice formations, namely icebergs in the Arctic seas, representing a threat to the safety of navigation and economic activity on the Arctic shelf are considered. Te main objective of the research is to develop methods for detecting icebergs using satellite radar data and high space resolution images in the visible spectral range. Te developed method of iceberg detection is based on statistical criteria for fnding gradient zones in the analysis of two-dimensional felds of satellite images. Te algorithms of the iceberg detection, the procedure of the false target identifcation, and determination the horizontal dimensions of the icebergs and their location are described. Examples of iceberg detection using satellite information with high space resolution obtained from Sentinel-1 and Landsat-8 satellites are given. To assess the iceberg threat, we propose to use a model of their drif, one of the input parameters of which is the size of the detected objects. Tree possible situations of observation of icebergs are identifed, namely, the «status» state of objects: icebergs on open water; icebergs in drifing ice; and icebergs in the fast ice. At the same time, in each of these situations, the iceberg can be grounded, that prevents its moving. Specifc features of the iceberg monitoring at various «status» states of them are considered. Te «status» state of the iceberg is also taken into account when assessing the degree of danger of the detected object. Te use of iceberg detection techniques based on satellite radar data and visible range images is illustrated by results of monitoring the coastal areas of the Severnaya Zemlya archipelago. Te approaches proposed to detect icebergs from satellite data allow improving the quality and efciency of service for a wide number of users with ensuring the efciency and safety of Arctic navigation and activities on the Arctic shelf.

Detection of iceberg using Delay Doppler and interferometric Cryosat-2 altimeter data

The Cryosat-2 Synthetic Aperture Interferometric Radar Altimeter (SIRAL) altimeter is the first altimeter that can operate in three different modes over the ocean: the classical pulse limited LRM, the Delay Doppler or SAR and the SAR Interferometric modes. It offers a unique opportunity to test, validate and compare the capabilities of the three modes for the detection and analysis of small icebergs (<3 km in length) already demonstrated for classical altimeters. Over most of the sea-ice free ocean, SIRAL operates in LRM mode and the classical iceberg detection algorithm can be applied without modification. It can also be applied to the Reduced SAR or pseudo-LRM data computed from SAR and SARin data. In SAR mode, iceberg signatures are bright spots in the waveform thermal noise part. They can be easily detected using classical image processing tools. The area of the iceberg is estimated using the size of the signature. In SARin mode, the coherence of the signals can insure the presence of scatterers above the sea surface and is used with the SAR detection algorithm to reduce the probability of false alarm and to better delineate icebergs. Interferometry allows for the first time to map the iceberg and the iceberg free-board at an unprecedented resolution opening a new way of investigation of the distributions of size, free-board and volume of the small icebergs that are responsible of large fraction of the freshwater flux into the ocean.

Iceberg Detection in Open and Ice-Infested Waters Using C-Band Polarimetric Synthetic Aperture Radar

Icebergs can cause a significant threat to shipping, offshore oil and gas production facilities, and subsea pipelines. Synthetic aperture radar (SAR) is a well-established tool for detecting and monitoring sea-ice objects in the often dark and cloud-covered polar regions. However, detection of small icebergs floating in nonhomegeous sea clutter environments is still a challenging task. We propose a new methodology for automatic identification of potential icebergs in high-resolution polarimetric SAR images. The algorithm adopts to various sea-ice conditions and it tackles high iceberg density situations and heterogeneous background conditions in the marginal ice zone. Results from a time series of RADARSAT-2 data containing numerous icebergs broken off from glaciers in Kongsfjorden on Svalbard demonstrate that the approach is viable.

Near field ice detection using infrared based optical imaging technology

If not detected and characterized, icebergs can potentially pose a hazard to oil and gas exploration, development and production operations in arctic environments as well as commercial shipping channels. In general, very large bergs are tracked and predicted using models or satellite imagery. Small and medium bergs are detectable using conventional marine radar. As icebergs decay they shed bergy bits and growlers, which are much smaller and more difficult to detect. Their low profile above the water surface, in addition to occasional relatively high seas, makes them invisible to conventional marine radar.Visual inspection is the most common method used to detect bergy bits and growlers, but the effectiveness of visual inspections is reduced by operator fatigue and low light conditions. The potential hazard from bergy bits and growlers is further increased by short detection range (<1km). As such, there is a need for robust and autonomous near-field detection of such smaller icebergs. This paper presents a review of iceberg detection technology and explores applications for infrared imagers in the field. Preliminary experiments are performed and recommendations are made for future work, including a proposed imager design which would be suited for near field ice detection.

Iceberg drifting and distribution in the Vilkitsky Strait studied by detailed satellite radar and optical images

This paper is devoted to the detection and identification of icebergs in the Russian Arctic Seas from the use of high- and medium-resolution radar and optical images from EROS-B, Radarsat-1, Radarsat-2, SPOT-4 and SPOT-5 Earth observation satellites. In July–September of 2011–2013, the SCANEX Research and Development Center, the Federal State Unitary Enterprise Atomflot, and other partner organizations provided operational satellite monitoring of icebergs in the Kara Sea and the Laptev Sea. More than 130 highly detailed optical and radar images were received and processed. The Vilkitsky Strait—one of the narrowest and most dangerous places within the Northern Sea Route—was chosen as an experimental polygon. As a result, iceberg location in the strait during the 2011–2013 navigation periods was analyzed, as were the iceberg size, area, drift direction, and height.

A Depolarization Ratio Anomaly Detector to Identify Icebergs in Sea Ice Using Dual-Polarization SAR Images

Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

On the Detection and Discrimination of Ships and Icebergs Using Simulated Dual-Polarized RADARSAT Constellation Data

. Maritime surveillance is an important application of synthetic aperture radar and is one of the objectives of the RADARSAT Constellation, the next generation of Canada's RADARSAT satellites. The RADARSAT Constellation will be able to collect both circular transmit, linear receive (CTLR) compact polarimetric data and linear dual-polarization data in a variety of wide swath imaging modes. In this article, we focus on the medium-resolution and low-resolution modes in order to assess the capability of the RADARSAT Constellation to both detect ships and icebergs in ocean imagery and then to discriminate between them after detection is performed. We first performed target detection, using a method whereby the false alarm rate was adjusted from pixel to pixel based on the properties of the received polarization ellipse. We then attempted to discriminate between the detected ship and iceberg targets using a support vector machine classifier. Measuring the discrimination accuracy using stratified 10-fold cross-validation, we found that the CTLR data had an accuracy of 99.3% for the medium-resolution imaging mode compared to 96.5% for the HH-HV dual-polarization. Further work is necessary using a wider variety of data, but RADARSAT Constellation data seem to have strong potential for ocean target detection and discrimination.Résumé. La surveillance maritime est une application importante du radar à synthèse d’ouverture, et elle représente un des objectifs de la Constellation RADARSAT, qui est la prochaine génération de satellites canadiens RADARSAT. La Constellation RADARSAT sera en mesure de recueillir à la fois des données de polarimétrie compacte CTLR (transmission circulaire, réception linéaire) et des données linéaires en polarisation double dans une variété de modes d’imagerie avec une large fauchée. Dans cet article, nous nous concentrons sur les modes à basse et moyenne résolution afin d’évaluer la capacité de la Constellation RADARSAT pour la détection à la fois des navires et des icebergs dans l’imagerie de l’océan, puis de les discriminer après que la détection est effectuée. Nous avons d’abord procédé à la détection de cibles, en utilisant un procédé par lequel le taux de fausses alarmes a été ajusté de pixel à pixel sur la base des propriétés de l’ellipse de polarisation reçue. Nous avons ensuite tenté de distinguer les navires des icebergs entre les cibles détectées, en utilisant un classificateur de machine à vecteurs de support. En mesurant la précision de la discrimination en utilisant une validation stratifiée croisée à 10 plis, nous avons constaté que les données CTLR avaient une précision de 99,3 % pour le mode de résolution d’imagerie moyenne, par rapport à 96,5 % pour la polarisation double HH-HV. Des travaux supplémentaires sont nécessaires en utilisant une plus grande variété de données, mais les données de la Constellation RADARSAT semblent avoir un fort potentiel pour la détection et la discrimination des cibles océaniques.

NAVIGATION ASSISTANCE FOR ICE-INFESTED WATERS THROUGH AUTOMATIC ICEBERG DETECTION AND ICE CLASSIFICATION BASED ON TERRASAR-X IMAGERY

Abstract. Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.

The History of US: From Bats and Boats to the Bedside and Beyond: RSNA Centennial Article.

The article traces the emergence of modern US from knowledge accumulated gradually over 2 centuries from scientific observation, including experiments with flying bats, underwater church bells, iceberg detection devices, and imaging systems that required the patient to be submerged in a tank of water.

A Review on Applications of Imaging Synthetic Aperture Radar with a Special Focus on Cryospheric Studies

The cryosphere is the frozen part of the Earth’s system. Snow and ice are the main constituents of the cryosphere and may be found in different states, such as snow, freshwater ice, sea ice, perma-frost, and continental ice masses in the form of glaciers and ice sheets. The present review mainly deals with state-of-the-art applications of synthetic aperture radar (SAR) with a special emphasize on cryospheric information extraction. SAR is the most important active microwave remote sensing (RS) instrument for ice monitoring, which provides high-resolution images of the Earth’s surface. SAR is an ideal sensor in RS technology, which works in all-weather and day and night conditions to provide useful unprecedented information, especially in the cryospheric regions which are almost inaccessible areas on Earth. This paper addresses the technological evolution of SAR and its applications in studying the various components of the cryosphere. The arrival of SAR radically changed the capabilities of information extraction related to ice type, new ice formation, and ice thickness. SAR applications can be divided into two broad classes-polarimetric applications and interferometric applications. Polarimetric SAR has been effectively used for mapping calving fronts, crevasses, surface structures, sea ice, detection of icebergs, etc. The paper also summarizes both the operational and climate change research by using SAR for sea ice parameter detection. Digital elevation model (DEM) generation and glacier velocity mapping are the two most important applications used in cryosphere using SAR interferometry or interferometric SAR (InSAR). Space-borne InSAR techniques for measuring ice flow velocity and topography have developed rapidly over the last decade. InSAR is capable of measuring ice motion that has radically changed the science of glaciers and ice sheets. Measurement of temperate glacier velocities and surface characteristics by using airborne and space-borne interferometric satellite images have been the significant application in glaciology and cryospheric studies. Space-borne InSAR has contributed to major evolution in many research areas of glaciological study by measuring ice-stream flow velocity, improving understanding of ice-shelf processes, yielding velocity for flux-gate based mass-balance assessment, and mapping flow of mountain glaciers. The present review summarizes the salient development of SAR applications in cryosphere and glaciology.

Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data

Iceberg monitoring is an important application of synthetic aperture radar (SAR), and is one of the stated objectives of the Radarsat Constellation, the next generation of Canada’s Radarsat satellites. In this paper, we simulate Radarsat Constellation data in a number of different imaging modes, using Radarsat-2 single-look complex data covering a study area in the Labrador Sea. We test the iceberg detection performance of both linear dual-pol data as well as compact polarimetry, a novel SAR architecture that transmits circular polarization rather than the traditional horizontal or vertical polarizations. We use the likelihood ratio test method to calculate a decision variable image for each of a number of different dual-pol and compact configurations, then analyze the detection performance using 25 validated iceberg locations spread across 12 different scenes. We found that compared to the linear data, the compact data missed fewer targets, and detected a greater number of pixels of detected targets, for most of the incidence angles and imaging modes tested. Compact polarimetry seems to be a promising choice for iceberg detection applications.

C-Band Radar Polarimetry—Useful for Detection of Icebergs in Sea Ice?

This paper is focused on investigations of polarimetric C-band radar signatures of icebergs in sea-ice-covered ocean regions. The main objective is to assess the potential improvement of iceberg detection when using radar polarimetry. The dominant backscattering mechanisms of icebergs are deduced by evaluating different polarimetric parameters. Magnitudes of the cross-polarization ratios, the correlation coefficients between HH- and VV-polarized signals, and the entropy/alpha parameters indicate a strong contribution of volume scattering in many cases. Over most icebergs, the phase differences between HH- and VV-polarization are larger than zero. Spatial patterns of the polarimetric parameters differ from iceberg to iceberg and between different parameters. On some bergs, they only exhibit slight variations, whereas on others, they show noiselike textures, but also, more systematic changes are observed. Occasionally, radar intensities of icebergs are of similar magnitude as those of sea ice. Only for a number of these cases, the combined use of the investigated polarimetric parameters together with intensity improves the discrimination performance between icebergs and sea ice.

Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar

In recent years, the use of synthetic aperture radar (SAR) for iceberg detection has been increasing thanks to the greater availability and coverage of SAR data, particularly polarimetric data. Greater amounts of polarimetric information can increase detection performance, preventing false alarms and missed detections. However, quad-polarization (quad-pol) SAR systems have increased data rate and power usage requirements, causing quad-pol modes to have generally half the swath width of dual-polarization (dual-pol) modes. Compact polarimetry is a compromise that allows the approximation of quad-pol information (referred to as “pseudo quad-pol”) using a dual-pol SAR. In this paper, using compact polarimetric data simulated from RADARSAT-2 quad-pol imagery, we show how pseudo-quad-pol data can be used for iceberg detection using the likelihood ratio test method. We show that use of the pseudo-quad-pol HV intensity can augment the detection performance of a compact polarimetric SAR system. We also compare the performance of the various compact polarimetric configurations to linearly polarized dual-pol and quad-pol data. [Traduit par la rédaction] Ces dernières années, le radar à synthèse d'ouverture (RSO) a été de plus en plus utilisé pour la détection des icebergs grâce à une meilleure disponibilité et une meilleure couverture des données RSO, en particulier des données polarimétriques. De plus grandes quantités d'informations polarimétiques peuvent améliorer la performance de détection, ce qui évite les fausses alarmes et les détections manquées. Cependant, les systèmes RSO à quadruple polarisation (quad-pol) exigent des débits binaires et une consommation d’énergie plus élevés, de telle sorte que les modes quad-pol utilisent généralement une largeur de couloir la moitié moindre que celle des modes à double polarisation (dual-pol). La polarimétrie compacte est un compromis qui permet d'approximer l'information quad-pol (alors appelée « pseudo-quad-pol ») au moyen d'un RSO dual-pol. Dans cet article, en utilisant des données de polarimétrie compacte simulées à partir de l'imagerie RADARSAT-2 quad-pol, nous montrons comment les données pseudo-quad-pol peuvent servir à la détection des icebergs au moyen de la méthode du test du rapport des vraisemblances. Nous montrons que l'utilisation de l'intensité HV pseudo-quad-pol peut augmenter la performance de détection d'un système RSO polarimétrique compact. Nous comparons aussi la performance des diverses configurations polarimétriques compactes aux données dual-pol et quad-pol linéairement polarisées.