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Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data
2021
Remote Sensing
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In this paper, the performance of C-band synthetic aperture radar (SAR) Gaofen-3 (GF-3) quad-polarization Stripmap (QPS) data is assessed for classifying late spring and summer sea ice types. ...
The investigation is based on 18 scenes of GF-3 QPS data acquired in the Arctic Ocean in 2017. ...
Acknowledgments: The authors would like to thank the National Satellite Ocean Application Service for providing the GF-3 SAR data. ...
doi:10.3390/rs13081452
fatcat:4shp4fc2ojbipjst7swg6uaiky
Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification ...
Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, ...
ACKNOWLEDGMENT The authors would like to thank the National Satellite Ocean Application Service for providing the GF-3 SAR data. ...
doi:10.1109/jstars.2022.3170732
fatcat:zdffwplbonfydg7qooqdosj7zm
Physically Explainable CNN for SAR Image Classification
[article]
2022
arXiv
pre-print
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A hybrid Image-Physics SAR dataset format is proposed for evaluation, with both Sentinel-1 and Gaofen-3 SAR data being experimented. ...
The results show that the proposed PGIL substantially improve the classification performance in case of limited labeled data compared with the counterpart data-driven CNN and other pre-training methods ...
Left: sea-ice classification dataset with manual annotations. ...
arXiv:2110.14144v2
fatcat:3kzyofwaenc73cxnffcvjeq75m
Table of contents
2021
IEEE Transactions on Geoscience and Remote Sensing
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Jiao 9224 SPB-Net: A Deep Network for SAR Imaging and Despeckling With Downsampled Data ................................ ...
Wang 9350 Measuring Deformed Sea Ice in Seasonal Ice Zones Using L-Band SAR Images .......................................... ...
doi:10.1109/tgrs.2021.3113108
fatcat:cjk7et65pfcfjgpzooytgbnghq
2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS Aug. 2019 2588-2593 Fully Polarized SAR imagery Classification Based on Deep Reinforcement Learning Method Using Multiple Polarimetric Features. ...
., +, JSTARS Aug. 2019
2878-2888
Fully Polarized SAR imagery Classification Based on Deep Reinforcement
Learning Method Using Multiple Polarimetric Features. ...
doi:10.1109/jstars.2020.2973794
fatcat:sncrozq3fjg4bgjf4lnkslbz3u
Multifrequency Spaceborne Synthetic Aperture Radar Data for Backscatter-Based Characterization of Land Use and Land Cover
2022
Frontiers in Earth Science
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The outputs of the classification approach on multisensor, multifrequency, and multi-polarization polarimetric synthetic aperture radar data have shown reasonable accuracy in classifying the land use and ...
The land use/cover classification was performed based on the scattering response of the scatterers using a support vector machine classifier. ...
Deep Learning-Based Models for Temporal Satellite Data Processing: Classification of Paddy Transplanted fields. Ecol. ...
doi:10.3389/feart.2022.825255
fatcat:dqcyo3agdzbxtp5t3jhazwf6ti
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang, ...
B., Dong, X., and Li, Y., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L., and Hanssen, R.F ...
Mounika, K., +, TGRS March 2019 1538-1544 Contextual Classification of Sea-Ice Types Using Compact Polarimetric SAR Data. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Deep Learning Meets SAR
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.10027v1 fatcat:sjdqhdimfjdufazmwihs7zftku
With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows. ...
Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. ...
for sea ice classification. ...
arXiv:2006.10027v2
fatcat:s3tiroz4qve6nbhavtz77fbis4
2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2021 116-126 Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning. ...
Ahmad, J.A., +, JSTARS 2021 8849-8863 Polarimetric Behavior for the Derivation of Sea Ice Topographic Height From TanDEM-X Interferometric SAR Data. ...
., Hyperspectral Image Superresolution via Deep Structure and Texture Interfusion; JSTARS 2021 8665-8678 Hu, J., see Feng, D., JSTARS 2021 12212-12223 Hu, J., Shen, X., Yu, H., Shang, X., Guo, Q., ...
doi:10.1109/jstars.2022.3143012
fatcat:dnetkulbyvdyne7zxlblmek2qy
Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review
2020
Remote Sensing
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Necessary preprocessing and preparation of data for developing classification models are then highlighted. ...
Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions ...
[271] extracted deep features of SAR polarimetric data using a CNN model, which was accompanied by dimensionality reduction through principal component analysis and followed by an SVM classifier with ...
doi:10.3390/rs12203338
fatcat:awufdmqg4bhgpi2cmsxy5b52pa
Review on Active and Passive Remote Sensing Techniques for Road Extraction
2021
Remote Sensing
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Part 3 presents the combined application of multisource data for road extraction. ...
Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. ...
from airborne SAR, GaoFen-3 and TerraSAR-X. ...
doi:10.3390/rs13214235
fatcat:iv4mot6n6vayhmnelwsz4okiym
Water-Body Segmentation for SAR Images: Past, Current, and Future
2022
Remote Sensing
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Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. ...
This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction ...
[62] further employed the H/A/α-Wishart to segment the water-body from the background with Gaofen-3 qual-polarization data. ...
doi:10.3390/rs14071752
fatcat:nslhmftmkvhsfnufnlqywzfdjq
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 4925-4933 GF-3 Polarimetric Data Quality Assessment Based on Automatic Extraction of Distributed Targets. ...
., +, JSTARS 2020 5088-5101
GF-3 Polarimetric Data Quality Assessment Based on Automatic Extraction
of Distributed Targets. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications
2021
Remote Sensing
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A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 ...
Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. ...
With the increasing number of satellite systems in space, and the development of data acquisition strategies and of advanced processing methods (e.g., machine and deep learning) based on combinations of ...
doi:10.3390/rs13142847
fatcat:jsh2gqsdkzhrhhetjpsw6f3aqi
Detailed Author Index
2021
2020 17th European Radar Conference (EuRAD)
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(NASU, Ukraine) 65 C A Ka-Band Solid-State Doppler Polarimetric Cloud Radar (EuRAD05-3) Kucharski, Maciej (IHP, Germany) 234 C Scalable 2×2 MIMO Radar Demonstrator with BPSK Data Communication at 79GHz ...
Continuous Wave Synthetic Aperture Radar with Photonic-Assisted Signal Generation and Dechirp Processing (EuRAD02-4) Surface Velocity Measurement for Chinese Gaofen-3 SAR Satellite (EuRAD14-5) Liu, Yang ...
doi:10.1109/eurad48048.2021.9337384
fatcat:ujxtlp5zsjg6rkhfrjc22okzka
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