Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR

Sun, Tao; Zhang, Guosheng; Perrie, William; Zhang, Biao; Guan, Changlong; Khurshid, K.S.; Warner, Kerri; Sun, Jian Heng

doi:10.3390/rs10121938

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Using simulated CP from RADARSAT-2 FP, attempts have been made to select the best approaches to discriminate first year and multi-year sea ice types [30,31,51,52]. More application studies were undertaken for wetland characterization [32], shoreline extraction [33,53], and ocean wind retrieval [54] using simulated CP from RADARSAT-2 within the framework of RCM mission preparation.…”

Section: Overview Of Sar Compact Polarimetrymentioning

confidence: 99%

Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada

et al. 2020

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Synthetic Aperture Radar (SAR) has been used in characterizing intertidal zones along northern Canadian coastlines. RADARSAT-2, with its full polarimetric information, has been considered for monitoring these vulnerable ecosystems and helping enhance the navigational safety of these waters. The RADARSAT Constellation Mission (RCM) will ensure data continuity with three identical SAR satellites orbiting together, providing superior revisit capabilities. The three satellites are equipped with multiple configurations, including single-polarization (HH, HV, VV), conventional (HH-HV, VV-VH, and HH-VV), hybrid (i.e., compact) dual polarization, and fully polarimetric (FP) modes. This study investigates the potential of the compact polarimetric (CP) mode for mapping an intertidal zone located at Tasiujaq village on the southwest shore of Ungava Bay, Quebec. Simulated RCM data were generated using FP RADARSAT-2 images collected over the study site in 2016. Commonly used tools for CP analysis include Raney m-delta classification and the hybrid dual polarizations RH-RV (where the transmitter is right-circular and the receivers are horizontal and vertical linear polarizations) and RR-RL (where the transmitter is right circular and the receivers are right-circular and left-circular polarizations). The potential of CP is compared with single, conventional dual-pol, and FP. The Freeman–Durden and Touzi discriminators are used for FP analysis. The random forest classifier is used as a classification approach due to its well-documented performance compared to other classifiers. The results suggest that the hybrid compact (RR-RL and RH-RV) dual polarizations provide encouraging separability capacities with overall accuracies of 61% and 60.7%, respectively, although they do not perform as well as conventional dual-pol HH-HV (64.4%). On the other hand, the CP polarimetric m-delta decomposition generated slightly less accurate classification results with an overall accuracy of approximately 62% compared to the FP Freeman–Durden (67.08%) and Touzi discriminators (71.1%).

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Section: Overview Of Sar Compact Polarimetrymentioning

confidence: 99%

Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada

et al. 2020

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“…They combine electromagnetic features (backscattering coefficients) with image processing features. Hence, in [18], the following image processing features are utilized: (1) texture features: local binary pattern (LBP) [49], the edge histogram descriptor (EHD) [50], Gabor wavelets [51] and gray-level co-occurrence matrix (GLCM) [52]; (2) color features: hue-saturation-value color histogram [53], MPEG-7 dominant color descriptor (DCD) [50], and MPEG-7 color structure descriptor (CSD) [53]. More specifically, in [18], they perform classification over dual-and single-polarized SAR intensity data using different techniques to produce pseudo colored RGB image and intensity images to make color and texture feature extraction possible.…”

Section: Related Workmentioning

confidence: 99%

“…US (2,2) kth neuron (22,22) (20,20) (10,10) (8,8) i , at the next layer l + 1 with the output, s l k , and weight, w l ki , of the current layer. One can write that each weight element's contribution over the output:…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…SAR systems can operate in many conditions where optic systems often fail, e.g., night time or severe weather conditions. Hence, they have been extensively used in various applications such as tsunami-induced building damage analysis with TerraSAR-X [1], ocean wind retrieval using RADARSAT-2 [2], oil spill detection using RADARSAT-1, ENVISAT [3], land use/land cover (LU/LC) classification with RADARSAT-2 [4], vegetation monitoring [5] using Sentinel-1, and soil moisture…”

Section: Introductionmentioning

confidence: 99%

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Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks

et al. 2019

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Accurate land use/land cover classification of synthetic aperture radar (SAR) images plays an important role in environmental, economic, and nature related research areas and applications. When fully polarimetric SAR data is not available, single-or dual-polarization SAR data can also be used whilst posing certain difficulties. For instance, traditional Machine Learning (ML) methods generally focus on finding more discriminative features to overcome the lack of information due to single-or dual-polarimetry. Beside conventional ML approaches, studies proposing deep convolutional neural networks (CNNs) come with limitations and drawbacks such as requirements of massive amounts of data for training and special hardware for implementing complex deep networks. In this study, we propose a systematic approach based on sliding-window classification with compact and adaptive CNNs that can overcome such drawbacks whilst achieving state-of-the-art performance levels for land use/land cover classification. The proposed approach voids the need for feature extraction and selection processes entirely, and perform classification directly over SAR intensity data. Furthermore, unlike deep CNNs, the proposed approach requires neither a dedicated hardware nor a large amount of data with ground-truth labels. The proposed systematic approach is designed to achieve maximum classification accuracy on single and dual-polarized intensity data with minimum human interaction. Moreover, due to its compact configuration, the proposed approach can process such small patches which is not possible with deep learning solutions. This ability significantly improves the details in segmentation masks. An extensive set of experiments over two benchmark SAR datasets confirms the superior classification performance and efficient computational complexity of the proposed approach compared to the competing methods.retrieval with Sentinel-1 [6], TerraSAR-X, and COSMO-Skymed [7]. A comprehensive list of fields and applications of SAR is available in [8].Ecological and socioeconomic applications greatly benefit from LU/LC classification, making SAR image classification the primary task. For example, forest biomass analysis investigated in [9] provides vegetation ecosystem analysis in Mediterranean areas. Further studies [10,11] focus on the relation between vegetation type and urban climate by questioning how vegetation types affect the temperature. Moreover, Mennis [12] analyzes the relationship between socioeconomic status and vegetation intensity and reveals that higher vegetation intensity is associated with socioeconomic advantage. However, accurate LU/LC classification is a challenging task especially for conventional machine learning methods due to several reasons: (1) existing speckle noise in SAR data, (2) requirement of pre-processing, i.e., feature extraction is especially needed for single-and dual-polarimetric cases to compensate for the lack of full polarization information, and finally, (3) the large-scale nature of SAR data.Nevertheless, there...

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“…Sun et al [6] develop ocean wind retrieval models for right circular-vertical and right circular-horizontal polarizations from the compact-polarimetry mode of the RADARSAT Constellation Mission (RCM), a set of three satellites just launched in 2019. The wind retrieval models are validated and contribute to temporal oceanography or atmosphere dynamic research based on RCM SAR data.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Sea Surface Roughness Observed by High Resolution Radar

et al. 2019

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Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR

Cited by 7 publications

References 20 publications

Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada

Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada

Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks

Sea Surface Roughness Observed by High Resolution Radar

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