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Second harmonic generation (SHG) microscopy is an important optical imaging technique in a variety of applications. This article describes the history and physical principles of SHG microscopy and its more advanced variants, as well as their strengths and weaknesses in biomedical applications. It also provides an overview of SHG and advanced SHG imaging in neuroscience and microtubule imaging and how these methods can aid in understanding microtubule formation, structuration, and involvement in neuronal function. Finally, we offer a perspective on the future of these methods and how technological advancements can help make SHG microscopy a more widely adopted imaging technique.

While remote observations of the linear polarization of five low-albedo near-Earth asteroids are available at large phase angles, space missions have collected materials from the surface of two of them and one of them is already back to Earth. The structure of the regolith on the surface may be different from that encountered on larger objects, because of their low gravity and thermal stress cycling. Dust particles crushed from low-albedo meteorites (i.e. Orgueil and Allende) are tentatively used as analogues to provide a better approach of such regoliths. The PROGRA2 experiment studies the light-scattering properties of dust particles of various size distributions under Earth’s gravity either deposited or with clouds lifted by an air-draught, as well as under μ-gravity conditions. Similar maximum in polarization (i.e. Pmax) values are obtained experimentally for dark particles deposited or in levitation, showing that multiple scattering is negligible. The increased sensitivity of PROGRA2 instruments, together with imaging techniques, makes it possible to study Pmax with increasing sizes of the lifted particles, up to mm-sizes and above. Our results confirm that particles constituting the regolith are mm-sized and may reach polarization values up to 50 per cent in good agreement with remote observations. Some materials are also suggested to be present on the surface of the particles. Also, huge agglomerates made by random ballistic deposition may be considered as relevant analogues e.g. for pebbles and boulders.

Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, one can find autonomous vehicles that are able to properly detect objects in the scene in good weather conditions; however, some improvements still need to be done when the visibility is altered. People claim that using some non-conventional sensors such as, infra-red or Lidar, combined with classical vision, enhances road scene analysis in optimal weather conditions. In this work, we present the improvements achieved using polarimetric imaging in the complex situation of some adverse weather conditions. This rich modality is known for its ability to describe an object not only by its intensity information, even under poor illumination or strong reflection. The experimental results have shown that, using a new multimodal dataset, polarimetric imaging was able to provide generic features for both good weather conditions and adverse weather conditions, especially fog. By combining polarimetric images with an adapted learning model, the different detection tasks under fog were improved by about 15% to 44%.

We demonstrate detection of ice formations on a road surface using a polarimetric radar operating at 87.5-92.5 GHz. The radar measures the scattering parameters of the surface at horizontal and vertical polarizations and their cross-polarization components. We demonstrate detection of ice for radar beam directed at up to 45⁰ angle of incidence with respect to the surface which allows for road surface characterization in front of a vehicle. The method used is based on a statistical approach where the 2-port scattering parameters are measured multiple times and used to calculate an average scatter coherence matrix representing the surface. The coherence matrix is then decomposed to eigenvalues/vectors, which are used to estimate polarimetric attributes such as target entropy(degree of randomness) and polarimetric pedestal (degree of depolarization). Through measurements of dry, ice-covered and wet road surfaces, we show that both entropy and depolarization are increased with respect to dry surface when a thin ice layer is formed, while their value decrease for the case of wet surface. It is also shown that these polarimetric attributes are not sensitive to surface roughness in dry conditions, minimizing the probability of false alarm due to road surface wear.

Polarimetrical imaging is a noninvasive optical technique of great interest in biophotonics since it has the capability of obtaining relevant information of biological samples, being useful, for instance, for the early detection of diseases or the classification of biological structures, both on animal and vegetal tissues. Different structures produce different outcomes when interacting with light due to their polarimetric properties such as depolarization, dichroism or retardance. An exhaustive polarimetric analysis of these characteristics can unveil the relation between the tissue inherent characteristics and its polarimetric response, enabling us to find the most appropriate polarimetric parameters to describe or study a sample. These polarimetric characteristics can be obtained through the experimental measurement of the Mueller matrix (M) of a sample, from which a range of different polarimetric observables, giving physical interpretation, can be deduced. By taking advantage of these parameters, we propose a study of the suitability of different groups of metrics for the contrast enhancement in biological tissues imaging, taking special attention on some depolarization metrics and some physical parameters such as the wavelength or the angle of incidence of the illumination light. The results obtained suggest the convenience of certain parameters which may be of interest in multiple biomedical scenarios such as pathology early detection or enhanced visualization of different structures for clinical applications.

Supernova remnants are commonly considered to produce most of the Galactic cosmic rays via diffusive shock acceleration. However, many questions regarding the physical conditions at shock fronts, such as the magnetic-field morphology close to the particle acceleration sites, remain open. Here we report the detection of a localized polarization signal from some synchrotron X-ray emitting regions of Tycho's supernova remnant made by the Imaging X-ray Polarimetry Explorer. The derived degree of polarization of the X-ray synchrotron emission is 9% ± 2% averaged over the whole remnant, and 12% ± 2% at the rim, higher than the value of polarization of 7%–8% observed in the radio band. In the west region, the degree of polarization is 23% ± 4%. The degree of X-ray polarization in Tycho is higher than for Cassiopeia A, suggesting a more ordered magnetic field or a larger maximum turbulence scale. The measured tangential direction of polarization corresponds to the radial magnetic field, and is consistent with that observed in the radio band. These results are compatible with the expectation of turbulence produced by an anisotropic cascade of a radial magnetic field near the shock, where we derive a magnetic-field amplification factor of 3.4 ± 0.3. The fact that this value is significantly smaller than those expected from acceleration models is indicative of highly anisotropic magnetic-field turbulence, or that the emitting electrons either favor regions of lower turbulence, or accumulate close to where the orientation of the magnetic field is preferentially radially oriented due to hydrodynamical instabilities.

International audience; A 3-in-1 depolarizing circular scatterer for the polarimetric radar calibration is proposed in this paper. The proposed scatterer is low cost, compact, planar circuit, and well suited for the radar operating in a compact range, for example, chipless RFID technology. By the virtue of its circular shape, the rear side of the proposed scatterer acts as a metallic disk with strong co polarization backscattered signals. The front side is composed of eight resonant dipoles which make it nondepolarizing and depolarizing scatterers at the inclination of 0° and 45°, respectively. The features of proposed circular scatterer are tested as reference calibration objects for single antenna based polarimetric radar calibration techniques. Two test objects are utilized: a dihedral tilted at 45° and a depolarizing multi resonant planar structure commonly called chipless RFID tag. The performance of proposed circular scatterer is also compared with the standard reference calibration objects: a metallic disk and a dihedral tilted at 22.5°. The performance of proposed circular scatterer is comparable to the standard reference calibration objects for the calibration techniques (namely Type 1 and Type 2). The proposed scatterer is potentially tolerant of displacement up to 1 cm and misalignment equals 2°.

Following the successful launch of the Spanish PAZ mission the proof of concept experiment ¿Radio Occultation and Heavy Precipitation with PAZ¿ (ROHP-PAZ) started operating in May 2018. The ROHP-PAZ observations demonstrated that precise measurements of the phase shift between horizontal and vertical polarizations from Global Navigation Satellite System (GNSS) L-band signals are sensitive to oriented hydrometeors along the ray paths. While this differential phase shift measurement as a function of time has proven very useful, the regular radio occultation (RO) intermediate products from different polarized channels, such as bending angle and phase retrievals on the domain of impact parameter, have never been exploited. In this research, we studied the characteristics of polarimetric phase and bending angle difference retrieved by the radio-holographic (RH) method to mitigate atmospheric multipath effect and to explore their use in data assimilation. To validate RH approach in polarimetric retrievals, we performed end-to-end simulations where the hydrometeors are modeled by the effective refractivity with different horizontal extents. The simulation results demonstrate that the strong precipitation (>15 mm h^-1) with 40-km horizontal extent can be detected with the retrieved bending angle shift. The calibration process on the impact parameter domain has also been developed to extract the differential phase and bending angle shift from the actual polarimetric RO data. Statistics from the PAZ data shows that the mean retrieved RH polarimetric phase shift with various horizontal extent is approximately proportional to the tangent point location rain rate at a ratio of 0.02 rad (mm h^-1)^-1, It is supported by NASA ROSES Earth Science U.S. Participating Investigator Program 14-ESUSPI14-0014. The GNSS PRO experiment aboard PAZ is funded by the Spanish Grant MCI RTI2018-099008-B-C21/AEI/10.13039/501100011033/ERDF. E.C. is member of the EUMETSAT ROM SAF.

In this letter, we propose a new scattering model to describe the polarimetric scattering information of the real part of T₂₃ in the coherency matrix. To achieve this goal, by combining the dihedral corner reflector scattering model and the polarimetric orientation angle (POA), a rotated dihedral scattering model is proposed. The proposed model is embedded into Singh's six-component decomposition model, and we further develop a seven-component decomposition model. The proposed method was validated by polarimetric synthetic aperture radar (SAR) data sets acquired by the ALOS-2/PALSAR-2 and AIRSAR systems. The results show that, compared with the existing decomposition methods, the proposed method has a superior ability to distinguish oriented buildings from vegetation.

Ship detection using polarimetric synthetic radar (PolSAR) imagery attracts a lot of attention in recent years. Most notably, the detector polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in PolSAR imagery, which gives excellent performances. In this work, a mathematical form of one new PNF (NPNF) based on physical mechanisms of targets and clutter is further developed for partial targets. The different mechanisms have been revealed based on the projection matrix. The experimental results including simulated and measured data demonstrate that the NPNF exhibits a better performance than the original PNF.

Understanding the optical properties of light reflected from snow surfaces is fundamental for remote sensing-based characterizations of snow properties and quantification of radiative transfer in the atmosphere-earth system. In theory, both intensity and polarization are required for describing the optical properties of light reflected from snow. However, thus far, a few studies have focused on the polarimetric properties of snow. In this study, we measured both multiangular photometric and polarimetric (based on the Stokes parameters) hyperspectral field results of snow with different properties (grain size, pollution levels, and microscopic surface roughness). By considering the absorption ratio of the polarizer, the intensity defined by photometric results [bidirectional reflectance factor (BRF) and hemispherical directional reflectance factor (HDRF)] was first confirmed to be similar to those derived from polarimetric measurements [I parameter reflectance factor (IpRF)] of snow surfaces. Then, the bidirectional polarized reflectance factor (BPRF) of snow, which is used to define the polarimetric properties of snow, was found to be useful for characterizing snow properties. Moreover, comparing ground measurements of snow BPRF with modeled results, we found that some existing BPRF models did not match well with the BPRF measured in the 2π space. These results suggest that more attention should be paid to the combination of IpRF (BRF or HDRF) and BPRF because polarimetric measurements can be used both for describing the optical properties of light reflected from snow and for quantifying snow properties.

Ionospheric Faraday rotation distorts satellite radar observations of the Earth's surface. While its impact on radiometric observables is well understood, the errors in repeat-pass interferometric synthetic aperture radar (InSAR) observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in nonquad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the copol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also, their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1-2 mm is of limited concern in many applications, the persistence on semiannual to multiannual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a nonnegligible source of bias in interferometric deformation measurements.

This letter proposes a machine learning inversion scheme for P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR), which can achieve the single-baseline random volume over ground (RVoG) model inversion without the assumption of the null ground-to-volume ratio. First, the potential variables--including the incidence angle and the PDHigh coherence acquired with phase diversity optimization--that are related to the forest vertical structure are analyzed for their correlations with the extinction coefficient in the RVoG model. Then, the machine learning approach is applied to forecast the extinction coefficient characterized by those potential variables. Ultimately, in the case of fixing the extinction coefficient, the forest height is estimated by a geometric process on the complex plane. The actual Pol-InSAR data verification illustrates that the inversion performance of the proposed scheme overmatches that of the traditional schemes.

Change detection is a key technology in the field of polarimetric synthetic aperture radar (PolSAR) image processing. The current research on the change detection mainly focuses on studying PolSAR images with the same angle or small angle difference, and the angle problem is not considered. However, when the angle difference occurs, especially a large angle difference, some pixels might be falsely detected because the angle difference can affect the polarimetric characteristics. In this letter, we propose a multilevel information fusion-based (MIFB) method, which is suitable for extracting change information from PolSAR images with angle difference. In particular, the proposed method first adopts data resolution correction, then applies an improved feature-based registration algorithm, and finally, incorporates weighted graph theory with the superpixel segmentation algorithm to extract and merge pixel-based and object-based change areas to eliminate false alarms. Experimental results for multitemporal and multiangle PolSAR images reveal that the MIFB method can effectively eliminate false detection caused by angle differences and improve the detection accuracy.

Noting the fact that the high-dimensional data composed of various polarimetric features has better decouplability than polarimetric synthetic aperture radar (PolSAR) image source data, in this letter, multiple polarimetric features are extracted and stacked to form a high-dimensional feature cube as the input of convolutional neural networks (CNNs) to improve the performance of PolSAR image classification. Directly utilizing the polarimetric features will produce a performance degradation and the recalibration of them is indispensable. However, classical feature selection methods are independent of the classifier, which means that the stimulated features may not be the classification-friendly ones. To avoid separated procedures and improve the performance, attention-based polarimetric feature selection convolutional network, called AFS-CNN, is proposed to implement end-to-end feature selection and classification. The relationship between input polarimetric features can be captured and embedded through attention-based architecture to ensure the validity of high-dimensional data classification. Experiments on two PolSAR benchmark data sets verify the performance of the proposed method. Furthermore, this work is quite flexible, which is reflected in that the proposal can be used as a plug-and-play component of any CNN-based PolSAR classifier.

The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor (Z) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m})$ estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w})$ , especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative Z bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content (W) and the rainfall rate (R) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , W, and R especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.

Polarimetric radar data from the WSR-88D network are used to examine the evolution of various polarimetric precursor signatures to tornado dissipation within a sample of 36 supercell storms. These signatures include an increase in bulk hook echo median raindrop size, a decrease in midlevel differential radar reflectivity factor (ZDR) column area, a decrease in the magnitude of the ZDR arc, an increase in the area of low-level large hail, and a decrease in the orientation angle of the vector separating low-level ZDR and specific differential phase (KDP) maxima. Only supercells that produced “long-duration” tornadoes (with at least four consecutive volumes of WSR-88D data) are investigated, so that signatures can be sufficiently tracked in time, and novel algorithms are used to isolate each storm-scale process. During the time leading up to tornado dissipation, we find that hook echo median drop size (D0) and median ZDR remain relatively constant, but hook echo median KDP and estimated number concentration (NT) increase. The ZDR arc maximum magnitude and ZDR–KDP separation orientation angles are observed to decrease in most dissipation cases. Neither the area of large hail nor the ZDR column area exhibit strong signals leading up to tornado dissipation. Finally, combinations of storm-scale behaviors and TVS behaviors occur most frequently just prior to tornado dissipation, but also are common 15–20 min prior to dissipation. The results from this study provide evidence that nowcasting tornado dissipation using dual-polarization radar may be possible when combined with TVS monitoring, subject to important caveats.

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm's effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.

For cumulative change detection using time-series polarimetric synthetic aperture radar (SAR) (PolSAR) data, pairwise comparisons using bi-temporal data change detection are often performed over each successive pair of images in the entire time series. In this way, due to the impact of the quality of the difference image (DI) of bitemporal data and the classification accuracy of the DI into the change and nonchange classes, there is a great risk of many false alarms or omissions caused by gradually occurred changes. Therefore, this letter proposes a symmetric similarity matrix based on a likelihood ratio test (LRT), which groups the similarity between any two-time point data in time series. Then, Shannon entropy is applied to the similarity matrix to calculate the DI of the entire time-series data. Finally, an image segmentation method based on Markov random field (MRF) is used to classify the calculated DI into the change and nonchange classes. The experiments using the multitemporal fully polarimetric Radarsat-2 data acquired at three different time points prove the effectiveness of the proposed method compared with some existing methods.

In this letter, different polarization characteristics of the sea clutter and target are considered to address the sea-surface weak target detection problem. We first construct a logarithmic generalized likelihood ratio test (log-GLRT) problem under the compound Gaussian model by considering multiple texture factors of sea clutter and four polarized channels. Based on the log-GLRT framework, a multivariable optimization problem is formulated to obtain the characteristic parameters of sea clutter and target. Then, under the block majorization-minimization (block-MM) framework, three parameters' estimation strategies are designed to solve the optimization problem of the proposed parameters. Finally, a log-GLRT-based polarimetric detector is proposed by applying the estimated characteristic parameters. The experimental results on the Intelligent PIxel processing Xband (IPIX) data sets demonstrate that our proposed detector can achieve better detection performance than the several existed detectors.

Oil spill accidents can cause severe ecological disasters, hence the timely and effective detection of oil spills on the marine surface is of great significance. Synthetic aperture radar (SAR) is very suitable for large-scale oil spill monitoring. As a more advanced form of SAR, polarimetric SAR (PolSAR) can provide more scattering information of land objects, which can help to improve the accuracy of oil spill detection. However, the current studies of oil spill detection by SAR data have mainly focused on using SAR intensity or amplitude information, and the phase information and other polarimetric information has not been fully utilized. To solve this problem, using Sentinel-1 dual-polarimetric images as the data source, this paper presents an intelligent oil spill detection architecture based on a deep convolutional neural network (DCNN), in which both the amplitude and phase information are utilized. Furthermore, to improve the feature discrimination capability, the Cloude polarimetric decomposition parameters are also integrated into the proposed model. The results show that the improved DeepLabv3+ model, which takes ResNet-101 as the backbone network and group normalization (GN) as the normalization layer, can achieve a superior performance than those traditional methods. Moreover, the model is better able to capture the fine details of oil spill instances and can achieve fine-scale segmentation.

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%-4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%-3% compared with QP. These conclusions can provide guidance for future payloads' design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems.

Scatter targets of complex land covers in polarimetric synthetic aperture radar (PolSAR) images are often randomly oriented and cause randomly fluctuating echoes, which brings a challenge to PolSAR image classification. Therefore, many existing methods have alleviated this problem through orientation compensation. However, there are still two obstacles that limit the improvement of classification accuracy. On the one hand, generally, these methods process PolSAR images with fixed polarization rotation angles, which is experience-dependent and inflexible. On the other hand, for the different land covers of a PolSAR image, the existing methods do not consider these rotation angles separately. For the first obstacle, we design a group of convolution kernels called polarization rotation kernels (PRKs) and utilize them to build the polarimetric convolutional neural network (CNN) (PolCNN). The PolCNN is the base network of our final model, and it can learn polarization rotation angles adaptively. For the second obstacle, we extend the PolCNN into a multipath structure, the final model polarimetric multipath CNN (PolMPCNN). The polarization rotation angles of different land covers are directly related to the networks of different paths within the PolMPCNN. Furthermore, we also put forward the two-scale sampling and the stagewise training algorithm in order that our PolMPCNN can fit different scales of PolSAR targets and pays more attention to difficult training samples. Experiments on real PolSAR images show that the proposed model achieves the best classification results with an extremely low sampling rate of 0.1%.

Interferometric phase optimization is important key processing for ensuring the application performance of interferometric synthetic aperture radar (InSAR) technology. The noise's standard deviation depends on the number of looks and the coherence magnitude. Usually, the coherence estimation uses statistical averaging with spatial samples to reduce the speckle noise in interferometric phase images. It has been demonstrated that polarization plays a significant role in the variation of interferometric complex coherence. Currently, InSAR technology utilizes polarimetric information to develop the coherence optimization theory for improving the phase quality. However, the observed coherence region in the complex unitary circle is usually biased from the free-noise one due to the finite multilooking effect and the practical scene heterogeneity, which makes the coherence optimization unstable. In contrast, based on the coherence estimation theory, this article proposes taking polarimetric information as the statistical samples for constructing polarimetric InSAR (PolInSAR) total power (TP) coherency matrix and performs a joint polarization-space nonlocal estimation. Simulated and real experimental results demonstrate that the proposed method improves the performance of the interferometric phase optimization in these three aspects compared with traditional coherence optimization, including phase quality improvement, the number of high coherent points, and computational efficiency.

For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over forest areas. In this letter, based on the TD + RVoG model, a new forest height estimation method is proposed for use with multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. First, the correlation between the ground-to-volume ratios (GVRs) associated with the different polarizations is parameterized according to the geometric interpretation of the RVoG model. An interferometric pair that is assumed to have no TD is then selected based on the eccentricity of the polarimetric coherence region, and the other interferometric pairs are fitted by the TD + RVoG model. E-synthetic aperture radar (E-SAR) P-band PolInSAR data sets affected by TD are used to prove the effectiveness of the proposed method. The experimental results show that the forest height results are improved by 25.90% when compared to the RVoG-based method.

The rotating phased array radar (RPAR) has the potential to improve the capabilities of the current U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) operational network and can be more affordable than other candidate phased array radar (PAR) architectures that have been evaluated to replace the WSR-88D. Considering the demanding functional requirements for the future U.S. weather surveillance radar, it is expected that several advanced RPAR scanning techniques will need to be applied simultaneously to achieve them. In this letter, we present the integration of two such RPAR scanning techniques: motion-compensated steering (MCS) and distributed beams (DBs). MCS exploits beam agility to mitigate beam smearing, while DB exploits digital beamforming to reduce the scan time or the standard deviation (SD) of estimates. The integration of these techniques is demonstrated with the National Severe Storms Laboratory's (NSSL) dual-polarization advanced technology demonstrator (ATD) radar system. Results show that these techniques can be used simultaneously to enhance azimuthal resolution and reduce the SD of estimates without impacting data quality if certain obtainable tradeoff considerations are incorporated in the radar design process.

This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the copolar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.

Time series of Sentinel-1 data are widely used for monitoring displacements of the Earth surface using persistent scatterer interferometry. By default over land, Sentinel-1 images include two polarimetric channels: 1) VV and 2) VH. However, most works in this application exploit only the VV channel, whereas the VH channel is discarded for its lower amplitude. Thanks to the development of polarimetric persistent scatterer interferometry methods, one can integrate multipolarization channels into a single optimal one. Previous studies proved that the number and spatial density of measurement points is increased. In this work, we explore the reason why the VH channel increases the number of measurement points when using the amplitude dispersion ( DA ) as selection criterion. Results obtained over three geographical locations show that the VH channel helps in two ways. In the first place, the mean amplitude is increased for targets which have higher amplitude in VH channel, usually associated with rotated elements in the scene. In second place, and more importantly, the amplitude dispersion is decreased over many areas for which the VV channel exhibits fluctuations and peaks. Thanks to the insensitivity of the VH channel to these scene changes, it provides additional measurement points which are reliable despite their low amplitude. The increment of measurement points not only extends the spatial density and enables the detection of active deformation areas not found in the VV results but also provides more accurate results than only using the VV channel, thanks to the increased density of points, which helps the deformation estimation. This work was supported in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) in part by the European Funds for Regional Development under Projects PID2020-117303GB-C21 and PID2020-117303GB-C22, and in part by the framework of the ESA-MOST China DRAGON-5 project with ref. 59339.

This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, C₂, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free C₂ matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of C₂, i.e., entropy (H₂), average alpha angle (α₂), and anisotropy (A₂), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating H₂, α₂, and A₂. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes.

Obtaining accurate atmospheric parameters, e.g., aerosol optical depth (AOD) and column water vapor (CWV), is important for the quantitative atmospheric correction (AC) of the high-spatial resolution remote sensing images. However, due to the strong temporal and spatial changes of the atmospheric parameters, it will be a challenge to ensure spatiotemporal registration of the satellite images given the AC parameters obtained separately from ground-based or other satellite products, which affects significantly the accuracy of the AC. The China National Space Administration launched a high resolution and multimode imaging satellite [Gao Fen Duo Mo (GFDM)] in July 2020, which has multifunctional observation modes and flexible mobility, with a high-spatial resolution imaging sensor (0.42 m in panchromatic and 1.6 m in multispectrum) and equipped the synchronization monitoring atmospheric corrector (SMAC) sensor. As the first atmospheric corrector with polarization detection capability on-board high-spatial resolution satellite, SMAC is designed to obtain multispectral intensity and polarized data and to retrieve synchronously AC parameters in the same field of view with main sensor. Based on the SMAC in-orbit test data, a lookup table method using the optimized inversion framework and a dual-channel ratio retrieval method are developed to derive AOD and CWV, respectively, in this article. The AOD and CWV results are validated against the AERosol RObotic NETwork (AERONET). The preliminary test of AC performance on the multispectral images of GFDM satellite indicates that SMAC is of great potential to improve the quality of the main sensor's image.

Man-made target detection of PolSAR image is an important part of the application of remote sensing data, and generally, the target occupies very few pixels of the image. Convolutional sparse representation (CSR) is proved able to effectively combine polarimetric and spatial information of a PolSAR image to achieve target detection. However, the acquisition of prior knowledge and the modeling of background are difficult. Moreover, the solution process is highly dependent on the alternating direction method of multipliers (ADMMs) algorithm, which introduces new parameters and has high computational complexity. To solve these problems, this letter proposes a man-made target detection method for PolSAR images based on local CSR (Local-CSR). In this method, a novel target detection framework to combine the polarimetric and spatial information based on CSR is proposed, the target dictionary is constructed by the Local-CSR to avoid the aforementioned problems of ADMM solution procedure and finally realize the man-made target detection of the PolSAR image. Two sets of fully polarimetric SAR data sets are used to demonstrate the performance and the experimental results prove the capacity and validity of the proposed method.

Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth data and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH) and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of the PLMR’s dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R) ≥ 0.864, root mean square error (RMSE) ≤0.04 m3/m3, and unbiased RMSE (unRMSE) ≤ 0.035 m3/m3), while the 3CH attempt yields the lowest uncertainty. Additionally, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.

The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages. To solves the problem of reduced resolution of PolSAR images due to system limitations, we propose a fully polarimetric synthetic aperture radar (PolSAR) images and single-polarization synthetic aperture radar SAR (SinSAR) images fusion network to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the polarimetric information of the low-resolution PolSAR (LR-PolSAR) images and the spatial information of the high-resolution single-polarization SAR (HR-SinSAR) images, we propose a fusion framework for joint LR-PolSAR images and HR-SinSAR images and design a cross-attention mechanism to extract features from the joint input data. Besides, based on the physical imaging mechanism, we designed the PolSAR polarimetric loss functions for constrained network training. The experimental results confirm the superiority of the fusion network over traditional algorithms. The average PSNR is increased by more than 3.6db, and the average MAE is reduced to less than 0.07. Experiments on polarimetric decomposition and polarimetric signature show that it maintains polarimetric information well.

Optical remote sensing is one of the most popular methods for estimating leaf nitrogen concentration (LNC). This nondestructive approach based on reflected intensity measurements has been applied to estimate the variation and distribution of nitrogen concentration in leaf and canopy levels in numerous studies. However, both intensity and polarization are necessary to describe the optical properties of light reflected from leaves and to estimate LNC estimation. In this study, based on the Stokes parameters, the total reflectance, the polarized reflectance, and the nonpolarized reflectance factors (NpRFs) were simultaneously obtained through polarimetric hyperspectral measurements under varied source-viewing geometries in both laboratory and field conditions. Several published hyperspectral indices based on the NpRF showed much better LNC estimation accuracy than those using the total reflectance factor. A clear improvement was found in the viewing directions dominated by specular reflection. Thus, using multi-angular polarimetric hyperspectral measurements not only improves the accuracy of hyperspectral indices on LNC estimation using the NpRF, but also enables the hyperspectral indices to be effective for a wide range of viewing angles. Moreover, polarimetric measurements deepen the understanding of the optical properties of light reflected from leaves. These results indicate that the combination of multiangular, polarimetric, and hyperspectral measurements may play a key role in the estimation of LNC.

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is y = 0.895x + 0.056, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD + 0.05). For the AEs, the regression line is y = 0.728x + 0.395, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

A novel distributed scatterer interferometric synthetic aperture radar (DS-InSAR) method is presented in which the sum of Kronecker product (SKP) decomposition method is applied to DS candidates. Unlike existing polarimetric optimization methods, the proposed method considers polarimetric and interferometric coherence information simultaneously, resulting in separation of the polarimetric scattering process for each target and the maximum diversity for the corresponding phase center locations. Physical reliability of the phase optimization solution is thereby ensured. The performance of the novel method is evaluated using 30 quad-polarized C-band Radarsat-2 synthetic aperture radar (SAR) images over Kilauea Volcano, Hawaii. The proposed method provides a higher density of measurement scatterer (MS) points and a higher quality of DS interferometric phase with a temporally stable phase center than single-polarization (HH) method and quad-polarization exhaustive search polarimetric optimization (ESPO) method. Thus, the proposed method shows good performance in phase quality improvement and point density increasement.

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems.

SAOCOM is a scientific and commercial mission developed by CONAE, the National Space Commission of Argentina. It is composed of two identical satellites, each one carrying a fully polarimetric L-band synthetic aperture radar (SAR). The main driver of the mission is the retrieval of soil moisture, an application that demands high radiometric and polarimetric accuracy. In this article, we summarize the main external calibration results obtained during the commissioning phase of the SAOCOM-1A satellite. In addition, the general calibration strategy as well as the devices and facilities that were involved in the external calibration process are described.

The 3-D inverse synthetic aperture radar (ISAR) tomography is an enabling technique for applications such as the exact diagnosis of scattering mechanisms for complex targets. Nevertheless, current ISAR tomography solutions still suffer from problems such as the great computational complexity and optimal utilization of the signatures acquired from limited baselines. In this work, we propose a fast ISAR tomography technique for fully polarimetric 3-D imaging of man-made targets. A stack of 2-D complex-valued images with different baselines and polarizations is first obtained through a phase error calibration (PEC) process and graphic processing unit accelerated polarimetric filtered backprojection. A polarimetric state-space decomposition (P-SSD) algorithm is then developed which could provide joint 3-D reconstruction results with low computational complexity. Examples from both numerical multibaseline data for the Sandia laboratories implementation of cylinders (SLICY) benchmark model and the outdoor range dataset collected by the Georgia Tech Research Institute (GTRI) are presented to demonstrate the superior performance and the usefulness of the proposed technique.

The scattering response of polarimetric synthetic aperture radar (PolSAR) data is strongly target orientation-dependent. Formulating the polarimetric matrix as sequential data by rotating the polarimetric matrix along the radar line of sight would provide rich information about land-cover properties. In this work, we propose a composite sequential network (CSN) with polarization orientation angle (POA) attention to model the polarimetric coherency matrix sequence and explore target scattering orientation diversity features. Three major factors strengthen the proposed method for PolSAR image analysis. First, CSN improves the feature comprehensiveness by extending the interpretation mode of PolSAR data from spatial polarization to spatial polarization orientation. In this way, CSN could describe polarimetric response dynamics at different orientations. Second, a two-stream composite network with both real- and complex-valued convolutional long short-term memory (ConvLSTM) network is proposed to process the diagonal and off-diagonal elements of the coherency matrix sequence, respectively. Compared to existing real-/complex-valued networks, the CSN explores the significant phase information of the off-diagonal elements by operations in the complex domain. Meanwhile, CSN prevents padding 0 meaninglessly in the imaginary part of the real-valued diagonal elements. Third, during the sequential modeling of the polarimetric matrix, a POA attention mechanism is proposed. Equipped with POA-sensitive decomposition loss, the CSN attends to substantial POA range derived by targets' physical scattering mechanism and learns features closely related to the scattering mechanism. Extensive experiments and analysis on land-cover classification demonstrate the proposed method's robustness and excellence.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local k-means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.

This talk outlines the latest observational advances in detecting and characterizing magnetic fields of late-type stars, from young pre-main-sequence (PMS) to mature and evolved objects. It focuses in particular on the latest magnetic measurements, for instance those of weakly-active M dwarfs and PMS stars collected over the last few years with the infrared spectropolarimeter SPIRou at the Canada-France-Hawaii Telescope, in the framework of the SPIRou Legacy Survey.

Рассматриваются вопросы реализации двойной и полной поляризации при зондировании Земли из космоса с использованием радиолокатора с синтезированной апертурой антенны (РСА). Рассмотрены одноканальные, двухканальные структуры приемо-передающих модулей (ППМ) активных фазированных решеток (АФАР). Особое внимание уделяется сигналам, применяемым для обеспечения полнополяризационного режима в двухканальном ППМ. В качестве квазиортогональных сигналов использованы сигналы с линейной частотной модуляцией (ЛЧМ) и бинарной фазовой кодовой манипуляцией (ФКМ). Показано, что для реализации развязки каналов 25…30 дБ необходимо применение сигналов с базами более 4000., The issues of the implementation of double and full polarization when probing the Earth from space using a synthetic aperture radar (SAR) are considered. The single-channel, two-channel structures of the receiving and transmitting modules (T/R modules) of active phased antenna arrays (APAA) are considered. Special attention is paid to the signals used to ensure a fully polarizing mode in a two-channel T/R module. Signals with linear frequency modulation (LFM) and binary phase shift keying (BPSK) were used as quasi-orthogonal signals. It is shown that in order to implement the decoupling of channels of 25...30 dB, it is necessary to use signals with bases of more than 4000.

The rotating phased array radar (RPAR) is an architecture that could improve the capabilities of the current weather surveillance radar--1988 Doppler (WSR-88D) operational network and is likely to be more affordable than other candidate PAR architectures. However, continuous antenna rotation coupled with the need to perform coherent processing of multiple samples results in a degraded effective beamwidth (referred to as beam smearing) compared to architectures based on stationary antennas. The RPAR's beam agility can be exploited to reduce beam-smearing effects by electronically steering the beam on a pulse-to-pulse basis within the coherent processing interval. That is, the motion of the antenna can be compensated to maintain the beam pointed at the center of resolution volume being sampled. This motion-compensated steering (MCS) could reduce the effects of antenna motion and lead to a reduction in the effective beamwidth. The purpose of this article is to present and demonstrate the MCS technique for a dual-polarization RPAR system. In this article, we provide a formulation for the MCS technique, simulations to quantify its performance in mitigating beam-smearing effects, its impacts on the quality of dual-polarization radar-variable estimates, and a practical implementation on the National Severe Storms Laboratory's Advanced Technology Demonstrator (ATD) system. Experiments were carried out using two alternative concepts of operations (CONOPS) described in this article. Results show that a system designed with sufficient pointing accuracy can be operated as an RPAR using MCS, and the impact on radar-variable estimates is comparable to that obtained when operating the same system as a stationary PAR.

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.