Exploring Dual-Polarimetic Descriptors for Sentinel-L Based Ship Detection

Pelich, Ramona; López-Martínez, Carlos; Chini, Marco; Hostache, Renaud; Matgen, Patrick; Ries, Philippe; Eiden, Gerd

doi:10.1109/igarss.2018.8517906

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…Scattering mechanisms classification based on polarimetric decomposition theorems in Dual-Pol SAR products are not recommended (Lee & Pottier, 2009;Ji & Wu, 2015), however, they are useful for probabilistic clustering functions such as unsupervised wishart classification. Entropy (H), Anisotropy (A) and Scattering mean angle (α) parameters are calculated from the decomposition of eigenvalues and eigenvectors of C2 matrix in (4) (Pelich et al, 2018).…”

Section: H/a/α Dual Polarimetric Decompositionmentioning

confidence: 99%

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

G.¹,

U.²,

A.³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Aquaculture fish cages are usually located remote areas with poor accessibility. Synthetic Aperture Radar (SAR) is relevant to register and monitor their activity. However, in order to take advantage of the large amount of information this technology produce, it is necessary to develop automated tools of analysis. This work proposes an automated approach through an unsupervised polarimetric classification method using Sentinel 1 IW SLC Dual-Pol (VV + VH) products. An experimental evaluation was applied in Calbuco, an area with intense aquaculture activity in southern Chile. It was possible to demonstrate that this approach allows to improve the capacity of classification of previous experiences (86.49% user’s accuracy and 96.97% producer´s accuracy). Further studies are required to know the impact of wind speed on the classification as well as the spatial precision of the detection.

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Section: H/a/α Dual Polarimetric Decompositionmentioning

confidence: 99%

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

G.¹,

U.²,

A.³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For extracting polarimetric information, we first extracted the C2 matrix (non-coherent covariance matrix) from the calibrated data. For dual polarimetric data, we used the Equation (1) to calculate the C2 matrix [40]. Data was scanned by IW method and was thus required to be debursted by the Sentinel-1 TOPS deburst method.…”

Section: Sentinel-1 Datamentioning

confidence: 99%

“…The range-doppler terrain correction method with the SRTM 1Sec HGT DEM [22] was used to georeference the C2 matrix. Finally, the dual-polarimetric H-A-Alpha decomposition was applied on the georeferenced C2 matrix [40]. Consequently, we used a 3 × 3 local window due to the available 17 m spatial resolution to extract information on sample plot level.…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Ataee

Maghsoudi

Latifi

et al. 2019

Forests

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Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1’s polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R2 and RMSE in a suggested GA fitness function. We calculated R2, RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model’s stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.

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“…In the recent years, many researches focus on the utilization of dual-polarimetric SAR data instead of quad-polarimetric SAR data for ocean targets observation or detection, as dual-polarimetric mode is often fixed for its larger swath widths compared to quad-polarimetric mode under the same resolution. Herein, the extensive use of TerraSAR-X dual-polarimetric SAR data has been explored for ocean surveillance, including ocean target investigation, coastline/shoreline extraction, sea ice analysis and other marine surveillance activities [3,[13][14][15][16][17][18][19]. Meanwhile, dual-polarimetric data from Radarsat-2 are also widely utilized for oil spill observation, ocean wind retrieval, and ocean target detection, as well as dual-polarimetric data from other satellite SAR systems [4,[20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…As the co-and cross-polarization HH/HV or VH/VV is the overused dual-polarization state in the satellite SAR systems, exploring HH/HV or VH/VV data attracts intensive attentions on ocean targets, especially for the man-made target observation or detection at sea [1][2][3][19][20][21][22]. Among these researches, many polarimetric scattering characteristic parameters are considered for ship detection, including the polarimetric entropy [1,2,19], phase difference [21] and reflection symmetry [1,3,20,22]. Herein, reflection symmetry, as an important symmetry property characterizing the natural background scenarios, was first exploited for metallic target observation at sea by Migliaccio [3].…”

Section: Introductionmentioning

confidence: 99%

Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data

et al. 2020

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The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific applications is ocean ship detection. Compared with the single polarization system, polarimetric systems can be used for more effective ship detection. In this paper, a generalized extreme value (GEV)-based constant false alarm rate (CFAR) detector is proposed for ship detection in the ocean by using the reflection symmetry metric of dual-polarization. The reflection symmetry property shows big differences between the metallic targets at sea and the sea surface. In addition, the GEV statistical model is employed for reflection symmetry statistical distribution, which fits the reflection symmetry probability density function (pdf) well. Five dual-polarimetric GF3 stripmap ocean data sets are introduced in the paper, to show the contrast in enhancement by using reflection symmetry and to investigate the GEV model fit to the reflection symmetry metric. Additionally, with the detection experiments on the real GF3 datasets, the effectiveness and efficiency of the GEV model for reflection symmetry and the model-based ocean ship detector are verified.

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Exploring Dual-Polarimetic Descriptors for Sentinel-L Based Ship Detection

Cited by 5 publications

References 6 publications

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

Improving Estimation Accuracy of Growing Stock by Multi-Frequency SAR and Multi-Spectral Data over Iran’s Heterogeneously-Structured Broadleaf Hyrcanian Forests

Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data

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