Iceberg and ship discrimination with ENVISAT multi-polarization ASAR

Howell, Carl; Youden, J.; Lane, K.; Power, Des; Randell, Charles; Flett, D.

doi:10.1109/igarss.2004.1368958

Cited by 14 publications

(12 citation statements)

References 3 publications

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“…Multi-polarization detection is an extension to the single CFAR method in that processing is on HH and HV channels independently, as described in [3].…”

Section: Data Processingmentioning

confidence: 99%

“…The features used in this study are: HH signal-to-clutter ratio (HHSCR), HV signal-to-clutter ratio (HHSCR), HVHH area ratio [3], HH mean σ ο , HH variance σ ο , HH maximum σ ο , HH mean σ ο (dB), HH variance σ ο (dB), HH maximum σ ο (dB) [4], area (pixel), major axis (pixel), minor axis (pixel), circularity, compactness, contour length, convexity, maximum diameter, anisometry, bulkiness, structure factor, inner circle radius, orientation, outer circle radius, outer rectangle length radius, outer rectangle width radius [5], HH iceberg incidence angle corrected average intensity (HHIA-AI), HH iceberg incidence angle corrected variance intensity (HHIA-VI), and HH iceberg incidence angle corrected maximum intensity (HHIA-MI).…”

Section: Data Processingmentioning

confidence: 99%

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A Multivariate Approach to Iceberg and Ship Classification in HH/HV ASAR Data

Howell¹,

Mills²,

Power³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Spaceborne synthetic aperture radar (SAR), with its wide area coverage and all-weather operation, is an ideal sensor to provide iceberg surveillance in support of safe shipping and offshore operations. Reliable ship/iceberg discrimination in SAR imagery is at least as important as detection since misclassification can result in expending significant resources for investigation or avoidance. To address this need, the authors have undertaken research to facilitate effective target discrimination using SAR multi-polarization data. The results presented here are for iceberg and ship classification for ENVISAT advanced synthetic aperture radar (ASAR) HH/HV data. Target classification was achieved by maximizing the a posteriori probabilities obtained from Bayes's rule.The maximum likelihood Gaussian classifier was used to model the probability of an unknown target belonging to either the iceberg or ship class. The feature selection algorithms, sequential forward selection (SFS), genetic algorithm (GA), and exhaustive search (ES) were evaluated for optimization of a feature space dependant multivariate classifier. The results from this study show for dual polarized HH/HV ASAR, icebergs and ships can be classified with a 93.5% accuracy using a two-class maximum likelihood model. As well, for the small sample set of 201 iceberg and ship targets presented here, suboptimal feature selection algorithms such as the SFS, GA, and exhaustive ranked search (ERS) are considered. These feature selection methods were considerably less computationally expensive to run than the global exhaustive search and were found to have converging results for both accuracy and features selected.

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“…Multi-polarization detection is an extension to the single CFAR method in that processing is on HH and HV channels independently, as described in [3].…”

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

A Multivariate Approach to Iceberg and Ship Classification in HH/HV ASAR Data

Howell¹,

Mills²,

Power³

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…Despite this lack of a polarimetric scattering model, polarimetric SAR has shown much promise in the detection of icebergs, with dual-polarization (dual-pol) systems being superior to single-polarization (single-pol) systems (Howell et al, 2004(Howell et al, , 2006(Howell et al, , 2008. Although fully polarimetric SAR data have been available for several years, to our knowledge there have been no published studies on its use for iceberg detection in the open literature.…”

Section: Introductionmentioning

confidence: 96%

Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar

Denbina

2012

Atmosphere-Ocean

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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.RÉSUMÉ [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 polarimé-tiques 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 RADAR-SAT-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.

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“…Target recognition is one of its most important applications. Many researchers have done a lot of work on target detection from SAR images 4‐6 . In Reference 7, an adaptive constant false alarm rate (CFAR) detection algorithm incorporating a clustering statistical model for target extraction from SAR image is presented.…”

Section: Introductionmentioning

confidence: 99%

Ship‐iceberg discrimination from Sentinel‐1 synthetic aperture radar data using parallel convolutional neural network

Song

Peters

Huang

et al. 2021

Concurrency and Computation

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Summary Ships and icebergs are similar in size and intensity in SAR images, so it is difficult to distinguish them in remote sensing images. Deep learning is a technique based on neural networks, which has played an important role in image information processing. In order to address the challenge of ship and iceberg classification, we present a convolutional neural network (CNN) based classification method for iceberg and ship discrimination from Sentinel‐1 SAR images with different polarizations and incidence angles. The method is based on the fixed constant false alarm rate (CFAR) detector and the CNN model has three input channels, then the model was trained using parallel algorithm. The CNN is trained using 1443 images and tested using 161 images. The CNN model is also compared with support vector machine (SVM) and k nearest neighbors (kNN) using the same dataset. Comparison shows the CNN‐based method performs the best, and it achieved a validation accuracy of 96%.

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Iceberg and ship discrimination with ENVISAT multi-polarization ASAR

Cited by 14 publications

References 3 publications

A Multivariate Approach to Iceberg and Ship Classification in HH/HV ASAR Data

A Multivariate Approach to Iceberg and Ship Classification in HH/HV ASAR Data

Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar

Ship‐iceberg discrimination from Sentinel‐1 synthetic aperture radar data using parallel convolutional neural network

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