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Journal articles on the topic 'Polarimetric Synthetic Aperture Radar'

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Published: 25 May 2024

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1

Raney, R. Keith. "Hybrid Dual-Polarization Synthetic Aperture Radar." Remote Sensing 11, no. 13 (June 27, 2019): 1521. http://dx.doi.org/10.3390/rs11131521.

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Compact polarimetry for a synthetic aperture radar (SAR) system is reviewed. Compact polarimetry (CP) is intended to provide useful polarimetric image classifications while avoiding the disadvantages of space-based quadrature-polarimetric (quad-pol) SARs. Two CP approaches are briefly described, π/4 and circular. A third form, hybrid compact polarimetry (HCP) has emerged as the preferred embodiment of compact polarimetry. HCP transmits circular polarization and receives on two orthogonal linear polarizations. When seen through its associated data processing and image classification algorithms, HPC’s heritage dates back to the Stokes parameters (1852), which are summarized and explained in plain language. Hybrid dual-polarimetric imaging radars were in the payloads of two lunar-orbiting satellites, India’s Earth-observing RISAT-1, and Japan’s ALOS-2. In lunar or planetary orbit, a satellite equipped with an HCP imaging radar delivers the same class of polarimetric information as Earth-based radar astronomy. In stark contrast to quad-pol, compact polarimetry is compatible with wide swath modes of a SAR, including ScanSAR. All operational modes of the SARs aboard Canada’s three-satellite Radarsat Constellation Mission (RCM) are hybrid dual-polarimetric. Image classification methodologies for HCP data are reviewed, two of which introduce errors for reasons explained. Their use is discouraged. An alternative and recommended group of methodologies yields reliable results, illustrated by polarimetrically classified images. A survey over numerous quantitative studies demonstrates HCP polarimetric classification effectiveness. The results verify that the performance accuracy of the HCP architecture is comparable to the accuracy delivered by a quadrature-polarized SAR. Four appendices are included covering related topics, including comments on inflight calibration of an HCP radar.

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2

Novak, L. M., and C. M. Netishen. "Polarimetric synthetic aperture radar imaging." International Journal of Imaging Systems and Technology 4, no. 4 (1992): 306–18. http://dx.doi.org/10.1002/ima.1850040410.

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3

El Assad, S., X. Morin, D. Barba, and V. Slavova. "Compression of Polarimetric Synthetic Aperture Radar Data." Progress In Electromagnetics Research 39 (2003): 125–45. http://dx.doi.org/10.2528/pier02053002.

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4

Rignot, E., and R. Chellappa. "Segmentation of polarimetric synthetic aperture radar data." IEEE Transactions on Image Processing 1, no. 3 (July 1992): 281–300. http://dx.doi.org/10.1109/83.148603.

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5

Thakur, P. K., R. D. Garg, S. P. Aggarwal, P. K. Garg, and J. Shi. "Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya." Cryosphere Discussions 7, no. 3 (May 3, 2013): 1927–60. http://dx.doi.org/10.5194/tcd-7-1927-2013.

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Abstract. The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.

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Ramana, K. V., P. Srikanth, U. Deepika, and M. V. R. Sesha Sai. "Polarimetric Synthetic Aperture Radar data for Crop Cover Classification." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 117–21. http://dx.doi.org/10.5194/isprsannals-ii-8-117-2014.

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The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.

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Adil, Muhammad, Andrea Buono, Ferdinando Nunziata, Emanuele Ferrentino, Domenico Velotto, and Maurizio Migliaccio. "On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship." Remote Sensing 14, no. 22 (November 17, 2022): 5813. http://dx.doi.org/10.3390/rs14225813.

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The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.

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8

Nord, M. E., M. E. Nord, T. L. Ainsworth, Jong-Sen Lee, and N. J. S. Stacy. "Comparison of Compact Polarimetric Synthetic Aperture Radar Modes." IEEE Transactions on Geoscience and Remote Sensing 47, no. 1 (January 2009): 174–88. http://dx.doi.org/10.1109/tgrs.2008.2000925.

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9

Ertin, E., and L. C. Potter. "Polarimetric calibration for wideband synthetic aperture radar imaging." IEE Proceedings - Radar, Sonar and Navigation 145, no. 5 (1998): 275. http://dx.doi.org/10.1049/ip-rsn:19982224.

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10

Hauter, Andrew, Kuo Chu Chang, and Sherman Karp. "Polarimetric fusion for synthetic aperture radar target classification." Pattern Recognition 30, no. 5 (May 1997): 769–75. http://dx.doi.org/10.1016/s0031-3203(96)00099-4.

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11

Denbina, Michael, and Michael J. Collins. "Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar." Atmosphere-Ocean 50, no. 4 (December 2012): 437–46. http://dx.doi.org/10.1080/07055900.2012.733307.

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12

El Assad, S., X. Morin, D. Barba, and V. Slavova. "Compression of Polarimetric Synthetic Aperture Radar Data — Abstract." Journal of Electromagnetic Waves and Applications 17, no. 5 (January 2003): 747–48. http://dx.doi.org/10.1163/156939303322226419.

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13

Zhang, Jin, Hao Feng, Qingli Luo, Yu Li, Jujie Wei, and Jian Li. "Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model." Remote Sensing 12, no. 6 (March 14, 2020): 944. http://dx.doi.org/10.3390/rs12060944.

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Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

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14

Phruksahiran, N., and M. Chandra. "Polarimetric radar cross section under SAR geometry." Advances in Radio Science 11 (July 4, 2013): 277–82. http://dx.doi.org/10.5194/ars-11-277-2013.

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Abstract. In this paper, the radar cross section of canonical scatter, with perfectly conducting surface, under the synthetic aperture radar geometry and polarized electromagnetic wave, has been considered and a new approach of polarized scattered electric field approximation for its evaluation has been developed.

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15

Zhang, Biao, Yiru Lu, William Perrie, Guosheng Zhang, and Alexis Mouche. "Compact Polarimetry Synthetic Aperture Radar Ocean Wind Retrieval: Model Development and Validation." Journal of Atmospheric and Oceanic Technology 38, no. 4 (April 2021): 747–57. http://dx.doi.org/10.1175/jtech-d-20-0035.1.

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AbstractWe have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

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He, Yijun, Hui Shen, and William Perrie. "Remote Sensing of Ocean Waves by Polarimetric SAR." Journal of Atmospheric and Oceanic Technology 23, no. 12 (December 1, 2006): 1768–73. http://dx.doi.org/10.1175/jtech1948.1.

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Abstract A new method to measure ocean wave slope spectra using fully polarimetric synthetic aperture radar (POLSAR) data was developed without the need for a complex hydrodynamic modulation transform function. There is no explicit use of a hydrodynamic modulation transfer function. This function is not clearly known and is based on hydrodynamic assumptions. The method is different from those developed by Schuler and colleagues or Pottier but complements their methods. The results estimated from NASA Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) C-band polarimetric SAR data show that the ocean wavelength, wave direction, and significant wave height are in agreement with buoy measurements. The proposed method can be employed by future satellite missions such as RADARSAT-2.

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17

Bai, Yanbing, Bruno Adriano, Erick Mas, and Shunichi Koshimura. "Identifying Building Damage Patterns in the 2016 Meinong, Taiwan Earthquake Using Post-Event Dual-Polarimetric ALOS-2/PALSAR-2 Imagery." Journal of Disaster Research 13, no. 2 (March 19, 2018): 291–302. http://dx.doi.org/10.20965/jdr.2018.p0291.

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The 2016 magnitude 6.4 Meinong earthquake caused catastrophic damage to peoples lives and properties in Taiwan. Synthetic Aperture Radar remote sensing is a useful tool to rapidly grasp the near real-time building damage to areas affected by the earthquake. Previous studies employed X-band single polarized high-resolution synthetic aperture radar imagery to identify building damage. However, suitable X-band single polarized high-resolution synthetic aperture radar imagery is not always accessible. Therefore, this research applied L-band dual-polarimetric ALOS-2/PALSAR-2 data to analyze the radar scattering characteristics of three types of affected buildings in the 2016 Meinong earthquake. The results show that collapsed buildings are characterized by a weak double-bounce scattering due to a reduced dihedral structure, while the characteristics of slightly damaged buildings are similar to those of undamaged buildings. Furthermore, the discrimination ability of a series of polarimetric, texture, and color features derived from the dual-polarimetric SAR data for three types of buildings affected by the earthquake are quantified based on a statistical analysis using the pixels in the combined areas of layover, shadow, and building footprint of each building. The results of the statistical analysis show that the spaceborne dual-polarimetric ALOS-2/PALSAR-2 images have good potential to distinguish between slightly damaged buildings and collapsed and tilted buildings. However, it is still difficult to distinguish between collapsed and tilted buildings. In addition, the results of the statistical analysis show that the mean value and variance value of the Gray-Level Co-Occurrence Matrix of the span image are two suitable features by which the categories of building damage can be distinguished. The polarimetric and color features demonstrated poorer performance in terms of distinguishing between damage categories than the texture features.

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18

Jaya, Laode M. Golok, Ketut Wikantika, Katmoko Ari Sambodo, and Armi Susandi. "Temporal Decorrelation Effect in Carbon Stocks Estimation Using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) (Case Study: Southeast Sulawesi Tropical Forest)." Forum Geografi 31, no. 1 (July 1, 2017): 99–107. http://dx.doi.org/10.23917/forgeo.v31i1.2518.

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This paper was aimed to analyse the effect of temporal decorrelation in carbon stocks estimation. Estimation of carbon stocks plays important roles particularly to understand the global carbon cycle in the atmosphere regarding with climate change mitigation effort. PolInSAR technique combines the advantages of Polarimetric Synthetic Aperture Radar (PolSAR) and Interferometry Synthetic Aperture Radar (InSAR) technique, which is evidenced to have significant contribution in radar mapping technology in the last few years. In carbon stocks estimation, PolInSAR provides information about vertical vegetation structure to estimate carbon stocks in the forest layers. Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric with 46 days temporal baseline were used in this research. The study was carried out in Southeast Sulawesi tropical forest. The research method was by comparing three interferometric phase coherence images affected by temporal decorrelation and their impacts on Random Volume over Ground (RvoG) model. This research showed that 46 days temporal baseline has a significant impact to estimate tree heights of the forest cover where the accuracy decrease from R2=0.7525 (standard deviation of tree heights is 2.75 meters) to R2=0.4435 (standard deviation 4.68 meters) and R2=0.3772 (standard deviation 3.15 meters) respectively. However, coherence optimisation can provide the best coherence image to produce a good accuracy of carbon stocks.

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Jung, Yoon, and Sang-Eun Park. "Comparative Analysis of Polarimetric SAR Calibration Methods." Remote Sensing 10, no. 12 (December 18, 2018): 2060. http://dx.doi.org/10.3390/rs10122060.

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In the diverse applications of polarimetric Synthetic Aperture Radar (SAR) systems, it is a crucial to conduct polarimetric calibration, which aims to remove the radar system distortion effects prior to utilizing polarimetric SAR observations. The objective of this study is to evaluate the performance of different polarimetric calibration methods. Two widely used methods, the Van Zyl and Quegan methods, and one recently proposed method, such as the Villa method, have been selected among various calibration methods in literature. The selected methods have basic differences in their assumptions that are applied to the polarimetric system model. In order to evaluate the calibration performances under different system parameters and ground characteristics, comparative analysis of the calibration results were conducted on synthetic polarimetric SAR data and ALOS PALSAR quad-pol mode data. Based on the experimental results, the advantages and limitations of different methods were clarified, and a simple hybrid calibration method is presented to further improve the polarimetric calibration performance.

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20

Li, Yu, Yuanzhi Zhang, Zifeng Yuan, Huaqiu Guo, Hongyuan Pan, and Jingjing Guo. "Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data." Sustainability 10, no. 12 (November 26, 2018): 4408. http://dx.doi.org/10.3390/su10124408.

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As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

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21

Merlo, Jason M., and Jeffrey A. Nanzer. "A C-Band Fully Polarimetric Automotive Synthetic Aperture Radar." IEEE Transactions on Vehicular Technology 71, no. 3 (March 2022): 2587–600. http://dx.doi.org/10.1109/tvt.2021.3138348.

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22

Migliaccio, Maurizio, Ferdinando Nunziata, Carl E. Brown, Benjamin Holt, Xiaofeng Li, William Pichel, and Masanobu Shimada. "Polarimetric synthetic aperture radar utilized to track oil spills." Eos, Transactions American Geophysical Union 93, no. 16 (April 17, 2012): 161–62. http://dx.doi.org/10.1029/2012eo160001.

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23

Sharifi, Alireza, and Jalal Amini. "Forest biomass estimation using synthetic aperture radar polarimetric features." Journal of Applied Remote Sensing 9, no. 1 (July 2, 2015): 097695. http://dx.doi.org/10.1117/1.jrs.9.097695.

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24

Drinkwater, Mark R., R. Kwok, D. P. Winebrenner, and E. Rignot. "Multifrequency polarimetric synthetic aperture radar observations of sea ice." Journal of Geophysical Research 96, no. C11 (1991): 20679. http://dx.doi.org/10.1029/91jc01915.

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25

Bouhlel, Nizar, and Stephane Meric. "Unsupervised Segmentation of Multilook Polarimetric Synthetic Aperture Radar Images." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8 (August 2019): 6104–18. http://dx.doi.org/10.1109/tgrs.2019.2904401.

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26

Dvorsky, Matthew, Mohammad Tayeb Al Qaseer, and Reza Zoughi. "Polarimetric Synthetic Aperture Radar Imaging With Radially Polarized Antennas." IEEE Transactions on Instrumentation and Measurement 69, no. 12 (December 2020): 9866–79. http://dx.doi.org/10.1109/tim.2020.3002443.

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27

Pincus, Paul, Mark Preiss, Alvin S. Goh, and Douglas Gray. "Polarimetric calibration of circularly polarized synthetic aperture radar data." IEEE Transactions on Geoscience and Remote Sensing 55, no. 12 (December 2017): 6824–39. http://dx.doi.org/10.1109/tgrs.2017.2734924.

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28

Zhang, Yi, Xingwei Jiang, Qingtao Song, Mingsen Lin, and Xuetong Xie. "Coastal wind field retrieval from polarimetric synthetic aperture radar." Acta Oceanologica Sinica 33, no. 5 (April 29, 2014): 54–61. http://dx.doi.org/10.1007/s13131-014-0479-5.

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29

Kuznetsov, V. A., and A. A. Potapov. "Texture-fractal analysis of polarimetric images generated by synthetic aperture radar stations." Радиотехника и электроника 68, no. 10 (October 1, 2023): 941–53. http://dx.doi.org/10.31857/s0033849423100145.

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All currently known methods and methods for the formation of fractalfeatures of polarimetric radar images. Briefly reviewed newtexture-fractal feature – a directional multifractal signature measured morphologically by the iterative covering method. In relation to the analysis of polarimetric images formed by a synthetic aperture radar, a new concept for their processing is proposed, based on the possibility of taking into account the polarization differences of ground-based spatially distributed objects by identifying the multifractal and anisotropic properties of their texture. A variant of interpretation of the obtained results for automatic optimization of further successful solution of specific segmentation (classification) problems underlying surface, detection and recognition of ground objects.

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Tang, Fanyi, Zhenfang Li, Qingjun Zhang, Zhiyong Suo, Zexi Zhang, Chao Xing, and Huancheng Guo. "Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion." Remote Sensing 15, no. 21 (November 3, 2023): 5224. http://dx.doi.org/10.3390/rs15215224.

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Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties.

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Susaki, J., and M. Kishimoto. "URBAN AREA EXTRACTION USING AIRBORNE X-BAND FULLY POLARIMETRIC PI-SAR2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 219–26. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-219-2015.

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In this paper, we present a method to extract urban areas from X-band fully polarimetric synthetic aperture radar (SAR) data. It is known that very high resolution (VHR) SAR can extract buildings, but it requires more processes to map urban areas that should include other objects. The proposed method is mainly composed of two classifications. One classification uses total power of scattering and volume scattering derived by using four component decomposition method with correction of the polarization orientation angle (POA) effect. The other classification uses polarimetric coherency between <i>S<sub>HH</sub></i> and <i>S<sub>VV</sub></i> . The two results are intersected and final urban areas are extracted after post-classification processing. We applied the proposed method to airborne X-band fully polarimetric SAR data of Polarimetric and Interferometric Airborne Synthetic Aperture Radar System (Pi-SAR2), developed by the National Institute of Information and Communications Technology (NICT), Japan. The validation show that the results of the proposed method were acceptable, with an overall accuracy of approximately 80 to 90% at 100-m spatial scale.

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Weissel, J. K., K. R. Czuchlewski, and Y. Kim. "Synthetic aperture radar (SAR)-based mapping of volcanic flows: Manam Island, Papua New Guinea." Natural Hazards and Earth System Sciences 4, no. 2 (April 19, 2004): 339–46. http://dx.doi.org/10.5194/nhess-4-339-2004.

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Abstract. We present new radar-based techniques for efficient identification of surface changes generated by lava and pyroclastic flows, and apply these to the 1996 eruption of Manam Volcano, Papua New Guinea. Polarimetric L- and P-band airborne synthetic aperture radar (SAR) data, along with a C-band DEM, were acquired over the volcano on 17 November 1996 during a major eruption sequence. The L-band data are analyzed for dominant scattering mechanisms on a per pixel basis using radar target decomposition techniques. A classification method is presented, and when applied to the L-band polarimetry, it readily distinguishes bare surfaces from forest cover over Manam volcano. In particular, the classification scheme identifies a post-1992 lava flow in NE Valley of Manam Island as a mainly bare surface and the underlying 1992 flow units as mainly vegetated surfaces. The Smithsonian's Global Volcanism Network reports allow us to speculate whether the bare surface is a flow dating from October or November in the early part of the late-1996 eruption sequence. This work shows that fully polarimetric SAR is sensitive to scattering mechanism changes caused by volcanic resurfacing processes such as lava and pyroclastic flows. By extension, this technique should also prove useful in mapping debris flows, ash deposits and volcanic landslides associated with major eruptions.

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Reisi Gahrouei, O., S. Homayouni, and A. Safari. "ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 885–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-885-2019.

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Abstract. The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop characterization. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in the management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which are essential for crops parameter modelling. The Earth observations used in this paper were collected by RapidEye satellites and Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system in the summer of 2012, over an agriculture area in Winnipeg, Manitoba, Canada. The RapidEye vegetation indices (VIs) and UAVSAR polarimetric parameters were used as inputs in artificial neural network (ANN) and support vector regression (SVR) models for canola biophysical parameters estimation. The best models were provided by SVR for canola. Also combining optical VIs and polarimetric features appeared as a powerful tool for crop parameters estimation in agricultural lands.

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Kong and Xu. "A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data." Sensors 19, no. 22 (November 6, 2019): 4839. http://dx.doi.org/10.3390/s19224839.

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A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

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Mittal, Vikas, Dharmendra Singh, and Lalit Mohan Saini. "Critical analysis of classification techniques for polarimetric synthetic aperture radar data." International Journal of Advances in Intelligent Informatics 2, no. 1 (April 16, 2016): 7. http://dx.doi.org/10.26555/ijain.v2i1.52.

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Full polarimetry SAR data known as PolSAR contains information in terms of microwave energy backscattered through different scattering mechanisms (surface-, double- and volume-scattering) by the targets on the surface of land. These scattering mechanisms information is different in different features. Similarly, different classifiers have different capabilities as far as identification of the targets corresponding to these scattering mechanisms. Extraction of different features and the role of classifier are important for the purpose of identifying which feature is the most suitable with which classifier for land cover classification. Selection of suitable features and their combinations have always been an active area of research for the development of advanced classification algorithms. Fully polarimetric data has its own advantages because its different channels give special scattering feature for various land cover. Therefore, first hand statistics HH, HV and VV of PolSAR data along with their ratios and linear combinations should be investigated for exploring their importance vis-à-vis relevant classifier for land management at the global scale. It has been observed that individually first hand statistics yield low accuracies. And their ratios are also not improving the results either. However, improved accuracies are achieved when these natural features are stacked together.

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Magagi, Ramata, Safa Jammali, Kalifa Goïta, Hongquan Wang, and Andreas Colliander. "Potential of L- and C- Bands Polarimetric SAR Data for Monitoring Soil Moisture over Forested Sites." Remote Sensing 14, no. 21 (October 24, 2022): 5317. http://dx.doi.org/10.3390/rs14215317.

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This study investigates the potential of L- and C- bands Polarimetric Synthetic Aperture Radar (PolSAR) data to monitor soil moisture over the forested sites of SMAP Validation Experiment 2012 (SMAPVEX12). The optimal backscattering coefficients and polarimetric parameters to characterize the soil moisture were determined based on L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), C-band RADARSAT-2, and ground measurements composed of soil and vegetation parameters collected during SMAPVEX12. Linear and circular backscattering coefficients (σ0) and polarimetric parameters such as correlation coefficients (ρHHVV) and phase difference (φHHVV) between HH and VV, pedestal height (PH), entropy (H), anisotropy (A), α angle, surface (Ps), and double bounce (Pd) powers were used to develop the relationships with soil moisture. The analysis of these relationships shows that over the forested sites of SMAPVEX12: (a) at L-band several optimal backscattering coefficients and polarimetric parameters allow the monitoring of soil moisture, particularly the linear and circular σ0 (r = 0.60–0.96), Ps (r = 0.59–0.84), Pd (r = 0.60–0.82), ρHHHV_30°, ρVVHV_30°, φHHHV_30° and φHHVV_30° (r = 0.56–0.81). However, compared to the results obtained with σ0, there is no added value of the polarimetric parameters for soil moisture retrievals. (b) at C-band, only a few polarimetric parameters φHHHV, φVVHV, and φHHVV are correlated with soil moisture (r = ~0.90). They can contribute to soil moisture retrievals over forested sites when L-band data are not available.

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Zhang, Yanmin, Yunhua Wang, and Qiaohui Xu. "On the Nonlinear Mapping of an Ocean Wave Spectrum into a New Polarimetric SAR Image Spectrum." Journal of Physical Oceanography 50, no. 11 (November 2020): 3109–22. http://dx.doi.org/10.1175/jpo-d-20-0045.1.

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AbstractA new nonlinear transformation relation is derived to describe the mapping of a two-dimensional ocean wave spectrum into a new polarimetric synthetic aperture radar (SAR) image spectrum. It is a further expansion and improvement of Hasselmann’s work. First, the nonlinear mapping relation proposed is derived on the basis of a new polarimetric SAR image instead of the conventional single-polarization SAR image. Second, the nonlinear mapping relation no longer includes the complex hydrodynamic modulation transfer function (MTF). Third, the traditional tilt MTF, which is not accurate enough for the retrieval of sea wave spectrum, is replaced by an empirical tilt MTF derived on the basis of the C-band geophysical model function [i.e., C-band synthetic aperture radar (CSAR) normalized radar cross section (NRCS) model]. A sea wave spectrum retrieval algorithm is then proposed that is based on the new nonlinear mapping and the empirical tilt MTF. The retrieved spectra from C-band polarized RADARSAT-2 SAR images are compared with the results obtained by the ECMWF Ocean Wave Model (ECWAM) and buoy measurements.

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Yu, Teng, Muyang Li, Weidong Li, Huafeng Mao, Rui Wang, Cheng Hu, and Teng Long. "Polarimetric Calibration Technique for a Fully Polarimetric Entomological Radar Based on Antenna Rotation." Remote Sensing 14, no. 7 (March 23, 2022): 1551. http://dx.doi.org/10.3390/rs14071551.

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For entomological radar, the polarization information of the target is usually used to estimate the biological parameters, such as orientation, body length, and mass, of the insect. Thus, the accuracy of polarization measurement directly affects the performance of the biological parameters’ estimation. The polarization measurement error is mainly caused by the imbalance of amplitude and phase between two polarization channels and the crosstalk of the dual-polarization antenna. In order to obtain the correct polarization information of the target, the polarimetric calibration of the entomological radar is required. This paper proposes a new polarimetric calibration technique based on antenna rotation, which does not require the calibrator to have a specific polarization scattering matrix (PSM). Compared with the currently existing calibration techniques, no prior knowledge of the calibrator PSM is required (in fact, any fixed-point target can be used as a calibrator); thus, the errors introduced by the mechanical process can be avoided. Simulations and data measured by radar verify the effectiveness of the method. This method has the potential to be extended to other fully polarimetric radar systems in the future, such as fully polarimetric weather radar, fully polarimetric synthetic aperture radar (SAR), and so on.

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Zheng, Honglei, Yanmin Zhang, Yunhua Wang, Xi Zhang, and Junmin Meng. "The polarimetric features of oil spills in full polarimetric synthetic aperture radar images." Acta Oceanologica Sinica 36, no. 5 (May 2017): 105–14. http://dx.doi.org/10.1007/s13131-017-1065-4.

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Martorella, M., A. Cacciamano, E. Giusti, F. Berizzi, B. Haywood, and B. Bates. "CLEAN Technique for Polarimetric ISAR." International Journal of Navigation and Observation 2008 (August 12, 2008): 1–12. http://dx.doi.org/10.1155/2008/325279.

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Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognising targets. To reduce the amount of data processed by the classifier, scattering centres are extracted from the ISAR image and used for classifying and recognising targets. This paper addresses the problem of estimating the position and the scattering vector of target scattering centres from polarimetric ISAR images. The proposed technique is obtained by extending the CLEAN technique, which was introduced in radar imaging for extracting scattering centres from single-polarisation ISAR images. The effectiveness of the proposed algorithm, namely, the Polarimetric CLEAN (Pol-CLEAN) is tested on simulated and real data.

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Fu, Wenxue, Xinwu Li, Meng Wang, and Lei Liang. "Delineation of Radar Glacier Zones in the Antarctic Peninsula Using Polarimetric SAR." Water 12, no. 9 (September 18, 2020): 2620. http://dx.doi.org/10.3390/w12092620.

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Climate change is a cause of the expansion of snowmelt phenomena in the Antarctic, and shifts in position of wet and dry snow lines have been considered as good indicators of climate changes. The impacts of climate change are observable by the delineation of significant position change of glacier zones. The principal limitation of current glacier zone classification methods by synthetic aperture radar (SAR) image is that it is difficult to discriminate dry-snow and wet-snow zones using only single-polarimetric radar backscattering intensity. This study tried to solve the problem using polarimetric SAR (PolSAR). Analysis indicates that polarimetric decomposition elements could be efficient characteristics to delineate radar glacier zones by recognition of principal backscatter patterns. Further, two radar glacier zone classification processes for polarimetric SAR are proposed: a supervised support vector machine (SVM) classification process and a simple decision-tree classification method. These methods enable reliable delineation of radar glacier zones in the Antarctic Peninsula. Polarimetric SAR, which provides more information about the scattering processes and target structure, proves to be an efficient tool for delineating radar glacier zones and snowmelt detection.

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Bailey, Johnson, Armando Marino, and Vahid Akbari. "Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data." Remote Sensing 13, no. 9 (April 30, 2021): 1753. http://dx.doi.org/10.3390/rs13091753.

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Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10−5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10−6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

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Kong, J. A., S. H. Yueh, H. H. Lim, R. T. Shin, and J. J. van Zyl. "Classification of Earth Terrain Using Polarimetric Synthetic Aperture Radar Images." Progress In Electromagnetics Research 03 (1990): 327–70. http://dx.doi.org/10.2528/pier90010100.

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Wang, Shuo, Haiming Qi, and Weidong Yu. "An Internal Calibration Scheme for Polarimetric Synthetic Aperture Radar System." IEEE Transactions on Geoscience and Remote Sensing 49, no. 1 (January 2011): 15–20. http://dx.doi.org/10.1109/tgrs.2010.2048716.

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Doulgeris, Anthony P., Stian Normann Anfinsen, and Torbjørn Eltoft. "Automated Non-Gaussian Clustering of Polarimetric Synthetic Aperture Radar Images." IEEE Transactions on Geoscience and Remote Sensing 49, no. 10 (October 2011): 3665–76. http://dx.doi.org/10.1109/tgrs.2011.2140120.

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Eltoft, Torbjorn, Stian Normann Anfinsen, and Anthony P. Doulgeris. "A Multitexture Model for Multilook Polarimetric Synthetic Aperture Radar Data." IEEE Transactions on Geoscience and Remote Sensing 52, no. 5 (May 2014): 2910–19. http://dx.doi.org/10.1109/tgrs.2013.2267615.

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Maurya, Himanshu, Amit Kumar, Amit Kumar Mishra, and Rajib Kumar Panigrahi. "Improved four‐component based polarimetric synthetic aperture radar image decomposition." IET Radar, Sonar & Navigation 14, no. 4 (February 21, 2020): 619–27. http://dx.doi.org/10.1049/iet-rsn.2019.0483.

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Lim, H. H., A. A. Swartz, H. A. Yueh, J. A. Kong, R. T. Shin, and J. J. van Zyl. "Classification of Earth terrain using polarimetric synthetic aperture radar images." Journal of Geophysical Research 94, B6 (1989): 7049. http://dx.doi.org/10.1029/jb094ib06p07049.

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Schuler, Dale L., Jong-Sen Lee, Thomas L. Ainsworth, and Mitchell R. Grunes. "Terrain topography measurement using multipass polarimetric synthetic aperture radar data." Radio Science 35, no. 3 (May 2000): 813–32. http://dx.doi.org/10.1029/1999rs002266.

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Kalinkevich, A. A., B. G. Kutuza, V. Yu Manakov, and V. A. Plyushchev. "Study of Coniferous Forests Using Multifrequency Polarimetric Synthetic-Aperture Radar." Journal of Communications Technology and Electronics 64, no. 12 (December 2019): 1339–47. http://dx.doi.org/10.1134/s1064226919120040.

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