In the Tianwen-1 Mars exploration mission, ultrawideband radar is carried by the Zhurong Martian rover. The exponential attenuation at the center frequency was applied to invert the loss tangent of Mars regolith in previous studies. Ignoring the frequency-dependent absorption in the ultrawideband might cause a large error in the inversion results. Considering the transmitted linear frequency-modulated (LFM) waves, in this letter, an analytical formula for the attenuation of ultrawideband waves to invert the loss tangents of Martian regolith is derived with the accumulation of the frequency-dependent attenuated spectrum. The newly inverted loss tangent is much larger than the inverted values with the center frequency. In addition, the inversion of the loss tangent from the ultrawideband radar data obtained in the Chang’e-5 lunar program is discussed. This letter presents a corrected inversion method for the loss tangent of regolith from ultrawideband radar echoes.

This study introduces a novel data assimilation framework, Dynamic Compressed Sensing-Dynamic Mode Decomposition(DCS-DMD), for real-time global four-dimensional(4-D) ionospheric electron density imaging and short-term prediction. Unlike traditional methods relying on complex predefined models, the framework employs a Koopman-based algorithm to extract time-varying ionospheric features and integrate them with observational data, enabling simplified and effective ionospheric imaging and prediction. Applied to the May 10–11, 2024 geomagnetic storm, the DCS-DMD model—using Global Navigation Satellite System(GNSS) and Radio Occultation (RO) data at a 5-minute resolution—outperforms existing models in tomographic accuracy. It shows significant improvement in differential slant total electron content(dSTEC) evaluations across reference stations at various latitudes, particularly when combining GNSS and RO data. The model also aligns well with ionosonde measurements, even during geomagnetic storms, detecting a density enhancement at the storm’s onset and a suppression during the recovery phase. Furthermore, the framework demonstrates effective short-term electron density prediction, highlighting its potential for forecasting foF2 in shortwave communication. This model significantly enhances the accuracy of ionospheric imaging and forecasting, providing a streamlined tool for space weather monitoring.

The paper proposes an optimal sensing principle for synthetic aperture radar (SAR) imaging, maximizing the mutual information between the sensed object and the reconstructed image with the optimal SAR measurement matrix. Inspired by Shannon’s capacity theorem, the 2-D SAR sensing capacity is derived, which represents the maximum mutual information that can be acquired per unit area in 2-D scenarios. The SAR sensing capacity serves as a theoretical performance bound, guiding the design of SAR sensing systems and enabling reasonable estimation of systems’ performance. Furthermore, the optimal sensing principle is applied to the variable-resolution SAR (VR-SAR) imaging system. An equivalent experiment With Sentinel 1 Raw Data is conducted to verify the advantages of VR-SAR based on the optimal sensing principle.

Accurate global-scale ionospheric electron density modeling is crucial for space weather monitoring, exploration, and radio signal applications. This letter presents a novel global-scale ionospheric tomography modeling method, dynamic compressed sensing-principal component analysis (DCS-PCA), building upon the previous region method CS-PCA. The upgraded method operates globally, utilizing dynamic data-driven techniques and undifferenced observation data processing to achieve high-precision quasi-real-time global-scale ionospheric tomography based on global navigation satellite system (GNSS) data. Tomographic models with a 5-min temporal resolution were constructed in this study, utilizing data from various IGS ground stations worldwide and employing the U-DCS-PCA, D-DCS-PCA, and traditional constrained algebraic reconstruction technique (CART). The DCS-PCA model is found to outperform both the CART model and the CODE Global Ionospheric Maps (CODG) model. Specifically, when evaluating differential STEC (dSTEC) errors at independent reference stations across various latitudes, we observed that the error of the DCS-PCA model is not significantly impacted by station sparsity, consistently remaining lower than that of CODG products. In contrast, the error of the CART model increases as the number of stations decreases. Additionally, the U-DCS-PCA model is found to closely align with electron density observations from ionosonde stations. This method is ideal for global 4-D ionospheric monitoring and has potential applications in space weather monitoring, exploration, and radio signal enhancement.

The ionosphere is a vitally dynamic charged particle region in the Earth’s upper atmosphere, playing a crucial role in applications such as radio communication and satellite navigation. The slant total electron contents (STECs) are an important parameter for characterizing wave propagation, representing the integrated electron density along the ray of radio signals passing through the ionosphere. The accurate prediction of STEC is essential for mitigating the ionospheric impact particularly on Global Navigation Satellite Systems (GNSS). In this work, we propose a high-precision STEC prediction model named deep neural operator network (DeepONet)-STEC, which learns nonlinear operators to predict the 4-D temporal-spatial integrated parameter for the specified satellite-ground station ray path globally. As a demonstration, we validate the performance of the model based on GNSS observation data for global and US Continuously Operating Reference Stations (CORS) regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 h prediction in quiet periods could achieve high accuracy using observation data by the precise point positioning (PPP) with temporal resolution$30~\rm {s}$. Under active solar magnetic storm periods, the DeepONet-STEC also demonstrated its robustness and superiority than traditional deep learning methods. This work presents a neural operator regression architecture for predicting the 4-D spatiotemporal ionospheric state for satellite navigation system performance, which may be further extended for various space applications and beyond.

Synthetic aperture radar (SAR) images are highly sensitive to observation configurations and exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance field (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection principles, a set of SAR images are modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3-D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of the 3-D voxel SAR rendering equation and the sampling relationship between the 3-D space voxels and the 2-D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multiview representation and generalization capabilities of SAR-NeRF. In addition, this article includes few-shot classification performance improvement as a metric for generation performance. The study found that using 12 images per class resulted in an accuracy improvement of nearly 10% for the classification algorithm.

Target recognition for synthetic aperture radar (SAR) images has been a longstanding hotspot. However, using polarimetric information to enhance recognition performance is under-researched. In this paper, we develop a visualization approach to analyze and highlight the contributions of polarimetric elements, and propose a simple polarimetric correlation feature for target recognition. In the visualization method, a channel-wise convolutional structure is well developed, which serves as a proxy of the polarimetric elements. By using the gradients of the target class flowing into each developed convolutional channel with normalization, we obtain activation maps indicating the contribution of each polarimetric element. Then a comprehensive quantitative evaluation of polarimetric element contribution is also performed. We demonstrate again that polarimetric information maintains significant advantages over single-polarization intensity, and the correlations between cross- and co-polarization emerge as key components for target recognition. Exactly inspired by these insights, the straightforward feature is defined as the correlation between the target and typical scatterers, seamlessly fusing intensities and correlations. This polarimetric correlation feature aptly encapsulates the physical scattering of the target, shows a clear mapping relationship with the geometric structure, and captures the differences between categories, achieving intra-category compactness and inter-category separation. Experimental results on both synthetic and real datasets underscore the effectiveness and superiority of this proposed feature.

The space situational awareness (SSA) is a critical issue of space security. The SSA of three-dimensional (3-D) information on flying space targets has been operated by inverse-SAR (ISAR) and telescope observations. However, how good it is to fuse these two observations, i.e. microwave and optical, has not been well discussed. Especially, it would be difficult to determine the dynamic attitude and its projections of a spinning target, only based on ISAR. This paper presents a co-location fusion of ISAR-optical images to acquire a 3-D reconstruction. Making use of semantic segmentation and parallelogram fitting, the dynamic parameters of the target can be acquired, and their projections can be determined. Instead of adopting a scattering point-based method, this paper presents a uniform projection representation by fusing the joint ISAR-optical silhouette-based 3-D reconstruction. This approach enables structure-level voxel reconstruction, even for a spinning target. The simulated datasets of the orthogonal projection geometry of fused space target ISAR-optical images demonstrate that good accuracy and favorable 3-D reconstruction can be achieved.

Speckle noise, a common artifact inherent in synthetic aperture radar (SAR) imagery, significantly degrades image quality. This deterioration in quality impedes critical tasks such as segmentation, object recognition, and other related applications. This paper introduces an innovative iterative trilateral filtering approach for SAR imagery enhancement, which is distinct in its use of Haar wavelet coefficients combined with pixel-domain distance confidence levels. By incorporating nonlocal (NL) information, the proposed approach aims to augment conventional wavelet transforms, effectively utilizing imprecise pixel data through confidence-weighted aggregations. The method uniquely integrates transform-domain, spatial-domain, and statistical properties of SAR images, which significantly enhances image clarity by producing high-fidelity despeckled outputs. Compared with traditional wavelet transforms, the proposed approach focuses on the discreteness analysis of inter-patch wavelet coefficients, which allows for deeper insights and better preservation of SAR image structures. The algorithm computes single-level Haar wavelet coefficients for patch pairs and determines wavelet weights based on inter-coefficient distances. Then, the algorithm calculates guided image pixel distances and reduces patch-wise Bhattacharyya distances to encode pixel distance confidence. These three weights are then consolidated for effective despeckling. To address directional blurring due to SAR image anisotropy, the conventional NL method’s square pixel vector is replaced with circular and annular vectors. The proposed approach demonstrates significant improvements in image integrity preservation, reducing speckle noise, as evidenced in our experiments with both simulated and real datasets.

The article proposes a generalized optimal sensing principle for synthetic aperture radar (SAR) imaging, maximizing the mutual information between the sensed object and the reconstructed image with the optimal SAR measurement matrix. Inspired by Shannon’s capacity theorem, the SAR sensing capacity is derived, which represents the maximum mutual information that can be acquired per unit distance or unit area in 1-D or 2-D scenarios. The SAR sensing capacity serves as a theoretical performance bound, guiding the design of SAR sensing systems and enabling reasonable estimation of systems’ performance. Additionally, this article analyzes the relationship between system parameters and the column correlation of SAR measurement matrices, guiding the design of the SAR system. Furthermore, the optimal sensing principle is applied to the variable-resolution SAR (VR-SAR) imaging system. Theoretical simulations are conducted to verify the feasibility of the optimal sensing principle, and examine the relationship between column correlation and SAR parameters. Additionally, the advantages of VR-SAR based on the optimal sensing principle are compared with those of the conventional strip-map SAR mode.

Synthetic aperture radar (SAR) has become a state-of-the-art technology in many applications without being affected by changes in weather and daylight. Since the detection capability of the single-dimensional SAR is limited, the multidimensional (MD) SAR system, e.g., multibaseline and multipolarization, is used to improve its performance. The design of the MDSAR system should be directly related to the specified applications and a quantitatively analytical theory for bound analysis is required to achieve good efficiency. In this article, a mathematical framework for inversion bounds analysis of MDSAR is proposed. First, based on the attributed scattering center (ASC) model, the Fisher information matrix and its corresponding Cramer–Rao lower bound (CRLB) are used to get the error bound of the estimated parameters. Second, considering the discrete sampling (DS) of the parameters, a probability density function-based conversion is conducted to get the DS CRLB. Finally, the mathematical framework for MD acquisitions is established. The simulation-based experimental results show that the theoretical error bound is consistent with the output of the orthogonal matching pursuit (OMP). The error bound of the parameters obtained by the proposed general mathematical framework can be used to evaluate the performance of inversion algorithms under certain MDSAR configurations.

Finding a flat area is critical for choosing the landing site, especially in the permanently shadowed region (PSR) without reference of optical images. The digital elevation model (DEM) data are not enough to describe the complex lunar topography in the landing mission. High-resolution polarimetric synthetic aperture radar (pol-SAR) can acquire the polarized scattering map of lunar surface. In this letter, Chandrayaan-2 pol-SAR images of flat and rocky regions at non-PSR are selected with the assistance of optical image. The statistics of the Stokes parameters can tell the difference between flat and rocky surfaces. Enhanced backscattering with a large variance from rough surfaces with rocks can be distinguished from the smooth and flat areas. Large value of the first Stokes parameter$I_{1}$with extendedly distributed probability distribution function is related with the scatterings from rough surface and rocks. A statistics rule of the Stokes parameters is established and is applied to identify the flat regions in the PSR of the crater Shoemaker.

Speckle reduction is a longstanding topic for polarimetric synthetic aperture radar (PolSAR) images. In this paper, we propose a novel end-to-end PolSAR image despeckling framework for the first time, which predicts the weight matrices of neighboring pixels instead of the target pixel itself nor the nor the noise, to achieve image despeckling. It hardly relies on any assumptions on the speckle noise distribution. Within this framework, residual in residual scaling network (RIRSN) is developed by combining the advantages of residual connections and residual scaling. To reduce network redundancy further, a dynamic version of RIRSN (DRIRSN) is also proposed by adjusting the network structure dynamically based on noise level and image content. Specifically, in DRIRSN, we introduce a lightweight network called picture2vector to estimate noise level, and a well-designed loss function to estimate image information level and measure image denoising quality simultaneously. The proposed picture2vector and loss function guide DRIRSN to focus on image areas with rich content and information, enhancing the adaptability of the network. DRIRSN inherits the properties of RIRSN for adaptively selecting and weighting the pixels of the neighborhood, and dynamically adjusts the network structure according to the estimated noise level and image content. We compare the proposed networks with reference methods on both simulated images and real images. Experimental results demonstrate that the proposed networks can effectively reduce speckle noise with low time consumption and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.

Quadric surfaces are commonly seen geometries in man-made targets. In this paper, a unified bidirectional scattering distribution function (BSDF) is analytically derived for general convex quadric surfaces including both the doubly- and the singly-curved surfaces. Based on physical optics (PO) and stationary phase method (SPM), the BSDFs of the doubly- and singly-curved surfaces are first deduced separately. Then the unified form of the two BSDFs is formulated, which can smoothly degenerate to any specific type of canonical curved surfaces by taking the corresponding values of the geometric parameters. Comparison with numerical PO demonstrates the correctness and efficacy of the proposed model. This model can be used to continuously model the bistatic polarimetric scattering behavior of a localized quadric surface patch. It can be used as the prototype for a scattering dictionary for both forward and inverse problems of electromagnetic scattering, which is of great value to radar target recognition and radar image interpretation.

The circular polarization ratio (CPR) was defined in compact-polarization (pol) mode as an indicator of water-ice in lunar PSR (permanently shadowed region). CPR is a composite pol-parameter described by co-pol and cross-pol scattering components, which caused by surface roughness and rocky objects on surface. In this paper, CPR is derived with linear-pol and circular-pol scattering components. Radar echoes from different rough surfaces are numerically simulated with the bidirectional analytic ray tracing (BART) method. The CPR, degree of polarization$(m)$and the relative phase$(\delta)$are numerically presented. As an example, Mini-RF radar images of the PSR crater Hermite-Band no- PSR crater Byrgius C are analyzed to illustrate how the roughness lead to different CPRs inside and outside the intermediately degraded craters.

Multi-channel moving target detection algorithm is an essential technique for ground moving target indication (GMTI). The inconsistency of magnitude and phase characteristics between channels will affect the clutter suppression effect of the Synthetic Aperture Radar (SAR) system, and thus affect the final performance of moving target detection. To solve this problem, a channel balance algorithm based on the two-dimensional Gaussian kernel function [1] and the non-local means (NLM) filter is proposed in this paper. Firstly, the phase error is estimated and compensated by the two-dimensional Gaussian kernel function in the image domain, and then the magnitude error is compensated by the mean filtering. Finally, the NLM filter is applied to further suppress clutter. The proposed algorithm can effectively eliminate magnitude and phase errors, and improve the coherence between different channels. Experimental results on real airborne SAR data demonstrate the effectiveness of our algorithm.

Direct sampling has never been performed in the permanently shadowed regions (PSRs) of lunar poles up to now. In the Chinese Chang’e-7 (CE-7) mission, a mini-flyer will fly from the lander in a solar illuminated region at the lunar south polar region to the nearby PSRs to collect samples for analysis. In this letter, four potential craters of the lunar south pole, including Shackleton, Shoemaker, de Gerlache, and Slater are discussed for this proposal. Design principles of the landing site, sampling site, and flight route are presented. The local surface slopes are calculated using a digital elevation model (DEM) to select a flat area as a potential landing site, which should allow ample time for solar illumination to support the rover from the lander and allow the flyer to reach the neighboring PSRs. Mini-RF data are applied for further validation of the flat landing and sampling sites, particularly for some rocky rough surfaces that are not identified in DEM and optical images of PSRs. The craters de Gerlache and Slater are found to be suitable for further analysis when high-resolution synthetic aperture radar (SAR) data are acquired by the new polarimetric SAR carried by CE-7.

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

The inherent magnitude and phase errors between channels in the multichannel synthetic aperture radar/ground moving target indication (SAR/GMTI) system will affect the clutter suppression and moving target detection performance of the system. To solve this problem, this letter proposes an adaptive channel balancing algorithm based on the 2-D Gaussian kernel function. First, the iterative reweighted least squares (IRLS) algorithm is used to estimate and compensate the inherent linear phase error that varies with the Doppler frequency in the range-Doppler domain. Then the Gaussian statistical analysis of the residual phase errors is performed in the image domain after the azimuth processing. According to the coupling characteristics of the range and azimuth of the SAR image, the correlation coefficient and the standard deviations of the phase errors in the range and azimuth directions are computed, and a 2-D Gaussian kernel function is constructed to estimate and compensate the residual phase errors. Finally, a mean filter is used to compensate the 2-D magnitude errors. This algorithm can eliminate the magnitude and phase errors and improve the coherence between different channels. The real airborne SAR data demonstrate the effectiveness of the proposed algorithm.

ISAR image data of a single target is sparse in the image domain. Based on this sparseness, we could obtain a high-precision image reconstruction by down sampling the imaging data and getting the sparse solution of the indeterminate equations. In this work, we have studied the sparse data processing theory based on the compressed sensing (CS) method. We focus on the sparse reconstruction of the inverse synthetic aperture radar (ISAR) image. The imaging data is sparsely sampled and restored through the norm regularization framework. We compare the reconstruction results onL1andL1/2regularization frameworks, respectively. Then, we concentrate on the relationship between the reconstruction results and parameter settings in the reconstruction framework. Besides, we study the ISAR image in different radar bands. The numerical results show that theL1/2regularization framework is better than theL1framework in recovery accuracy and computational efficiency.

Data augmentation is beneficial when the measured training data are insufficient to train a robust deep model. One of the promising techniques is to use simulated data generated by physics-based engines. For example, few-shot learning of synthetic aperture radar (SAR) targets could be benefited from simulated SAR images. However, the characteristics of the simulated training data significantly affect the performance of the trained model. Therefore, it is of great significance to evaluate the utility of simulated data objectively and effectively. A recognition rate versus substitution rate curve (RSC)-based assessment criterion is proposed, consisting of substitution rate (SR)-based dataset allocation stage and RSC-based evaluation stage. First, the differential dataset allocation is performed under a progressive SR to obtain paired reference and comparison training sets. Then, the reference and comparison classifiers are trained under different SRs using the same network and parameter configuration in the RSC criterion-based evaluation stage. AconvNet and AlexNet are selected as the backbones of the evaluation network. Especially, k-fold cross-validation is applied to alleviate selection bias. The difference between the integrals of RSCs is defined as the RSC score for the simulated dataset. Experiments conducted on the measured and simulated moving and stationary target acquisition and recognition (MSTAR) database demonstrate the rationality and validity of the proposed RSC criterion. Specifically, multisource simulated datasets are adopted, including the adversarial autoencoder-generated and electromagnetic simulation datasets. The proposed RSC criterion shows promising utility evaluation ability, flexibility, and extensibility compared with traditional full-reference image-quality assessment criteria.

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

High circular polarization ratio (CPR) characteristics were found in permanently shaded regions (PSRs) near the lunar poles. High CPR was regarded as a water ice index. The compact-polarimetric (CP) miniature radio frequency (Mini-RF) radar transmits left-circularly polarized signals and receives horizontally polarized ($S_{\text {HL}}$) and vertically-polarized ($S_{\text {VL}}$) echoes from the lunar surface. Statistics of the CPR data show its relations with the relative phase ($\delta$) between$S_{\text {HL}} $and$S_{\text {VL}} $and the degree of polarization ($m$) but few interpretations were provided. The average CPR data reach the maximum and minimum at$\delta =\pm 90^{\circ }$, respectively. As$m$becomes very small, the CPR approaches 1. It has been found that CPR is also affected by surface roughness and incidence angle of radar waves. The CPR is now expressed in CP mode to explain the Mini-RF observation. Full-polarimetric radar echoes and CP parameters of the lunar surface are numerically simulated using the bidirectional analytic ray-tracing method. Single-bounce and multiple-bounce scattering components are included in the simulation. Radar images of the lunar crater are simulated with the digital elevation model (DEM) data. The$H-\alpha $decomposition derived from the full-polarimetric simulation is presented to analyze$\delta $and$m$. Simulated radar images with different surface roughness are analyzed statistically to study the functional dependences of$\delta $,${m}$, and CPR on incidence angle and roughness. Relationships among$\delta $,$m$, and CPR are used to analyze the effects of incidence angle, roughness, TiO2, and rock abundance on the scattering components. The CPR,$m$, and$\delta $of PSR craters of different ages are compared with those of nonpolar craters. The results indicate that the CPR,$m$, and$\delta $are unlikely to be unambiguous evidence of water ice.

The Moon’s surface shows long-term stability and may be a suitable candidate of thermal calibration source. When the field of view (FOV) of an Earth radiometer, e.g., the high-resolution infrared radiation sounder (HIRS), occasionally scans across the Moon, it measures the average brightness temperature (TB) of the entire nearside of the Moon. The lunar surface roughness causes changes in the surface emissivity and anisothermality. Analysis of the infrared TB (IR TB) of the lunar surface from the nadir observations of the Diviner IR radiometer showed that the IR TB difference (TB-D) between the Diviner channels is largest near sunrise and sunset, when the anisothermality is most significant. In this article, the nearside of the lunar surface is divided into$30\times 30$subregions, where a rough surface is constructed using small triangular meshes. The ray-tracing method is used to determine the shaded meshes. Heat conduction equations are solved for the temperatures of all meshes. The simulated IR TB and TB-D are compared with the Diviner data. The thermal emissions of Gaussian rough surfaces with different height variances, the cratered surface with digital elevation model, and two-scale rough surface are simulated to discuss the influence of topographies. Under nonnadir observation conditions, the ray-tracing method is used to determine the visibility of each mesh in the detector’s FOV. The IR TB characteristics are asymmetric with respect to the emission angle. The average IR TB of the nearside of the Moon at the wavelength of$14~\mu \text{m}$is obtained and compared with the IR TB derived from HIRS data. The rough surface model can more feasibly describe the average IR TB data than flat surface model and is favorable for HIRS calibration/validation.

In airborne array interferometric SAR (Array-InSAR) tomography, the measurements acquired by conventional uniform sampling array are always restricted by the number of physical baseline elements and the size of baseline aperture. It is desirable to capture new acquisitions and enlarge the aperture with virtual signal processing instead of actually adding array baselines. For this motivation, we utilize the disparity of a pair of coprime sampling sub-arrays to enlarge the baseline aperture and construct new observations virtually. The generation of virtual measurements is equal to estimating cross-correlation matrices in real SAR data. Due to the spatial target variation, we adopted an adaptive filtering method to estimate the cross-correlation matrix. We call the above-mentioned processing of generating virtual measurements as acoprime sensing technique. The newly generated virtual measurements have more degrees of freedom, a larger baseline aperture, and a higher signal-to-noise ratio (SNR) than the physical measurements. These advantages offer the possibility to obtain competitive three-dimensional (3-D) imaging results without increasing the hardware cost of the Array-InSAR. We demonstrate the effectiveness of the proposed method by the coprime acquisitions selected from AIRCAS Array-InSAR data.

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due to scattering scintillation. Thus, data-driven SAR target recognition is considered a typical few-shot learning (FSL) task. This article first reviews the key issues of FSL and provides a definition of the FSL task. A novel adversarial autoencoder (AAE) is then proposed as an SAR representation and generation network. It consists of a generator network that decodes target knowledge to SAR images and an adversarial discriminator network that not only learns to discriminate “fake” generated images from real ones but also encodes the input SAR image back to target knowledge. The discriminator employs progressively expanding convolution layers and a corresponding layer-by-layer training strategy. It uses two cyclic loss functions to enforce consistency between the inputs and outputs. Moreover, rotated cropping is introduced as a mechanism to address the challenge of representing the target orientation. The moving and stationary Target recognition (MSTAR) 7-target dataset is used to evaluate the AAE’s performance, and the results demonstrate its ability to generate SAR images with aspect angular diversity. Using only 90 training samples with at least 25° of orientation interval, the trained AAE is able to generate the remaining 1748 samples of other orientation angles with an unprecedented level of fidelity. Thus, it can be used for data augmentation in SAR target recognition FSL tasks. Our experimental results show that the AAE could boost the test accuracy by 5.77%.

3-D computerized ionospheric tomography (CIT) is an ill-posed problem due to the insufficient amount of observations, it remains challenging for practical applications. In this article, we proposed an ionospheric tomography method that combined data-driven methods with compressed sensing (CS) to deal with the ill-posed problem. First, slant total electron content (STEC) data were extracted by undifferenced and uncombined precise point positioning (UCPPP) with known fixed station coordinates. Second, data-driven methods were adopted to construct the projection matrix from the ionospheric model. Third, compressed sensing was used to derive the sparse solution based on$L_{1}$norm. The ionospheric tomography can be achieved well by using observations during the shorter time interval and in a sparse receiver distribution based on the property of compressed sensing. Results of experiment based on real Global Positioning System (GPS) observation data verified the effectiveness of the proposed methods. By comparing with the colocated ionosonde, it is found that the CS methods are more consistent with the actual ionospheric fluctuation than the modified constrained algebraic reconstruction technique (CART). In terms of the differential STEC (dSTEC) analysis, the error of the tomography model by Compressed Sensing-Principal Component Analysis (CS-PCA) is less than 0.2 TEC unit (TECU), and the time resolution is 5 min. The UCPPP with constraint by CS-PCA shows the best performance of 12.2%, 40.9% and 0.31% improvement in positioning accuracy, convergence time, and fixed rate over the UCPPP with constraint by modified CART. The proposed data-driven methods may be important for high-resolution 4-D ionospheric tomography in the future.

Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging technology are powerful tools to acquire high-resolution radar image, which is an important basis for further automatic target recognition (ATR). For ISAR, if the radar frequency is high enough, signals that the radar received usually have strong sparsity when the radar data convert to Fourier domain, and they can be downsampled and restored by compressed sensing (CS). As for the regularization method in CS theory, while L₁ regularization is popular, the Lq(0 < q < 1) regularization is proved to be a sparse regularization framework and could achieve well performance. In this work, inspired by the L₁-based complex approximated message passing (CAMP) method and the L(1/2) regularization framework, we extend the CAMP method into an L(1/2)-based iterative thresholding method in ISAR imaging under the downsampling rate of the scattered fields. The matched filtering (MF) method is widely used to generate the SAR image, where targets are usually overwhelmed by noise and scene background. In order to make the target enhanced to further improve recognition, regularization is adopted in recovering sparse solution and the clutter reduction of MF image. This work implements the L(1/2)-based regularization into the SAR image target enhancement and the scene-noise reduction via CAMP. The SAR image is generated by ideal point scatterers and the real measurement data of RADARSAT-1. Given the sparsity estimated, the experiment results show the advantage of L(1/2) regularization compared with the L₁ regularization.

A generalized synthetic aperture radar (SAR) modality of operation named variable-resolution (VR) SAR is proposed, which explores the diversity of antenna patterns and inhomogeneity of pulse repetition frequencies (PRFs) for adaptive imaging. Based on the relationship between the azimuth resolution and the corresponding integration angle, it uses dynamic beam patterns along the trajectory to illuminate different regions. We formulate the optimization problem of VR SAR based on the principle of maximum mutual information. First, the information content of a specific scene is defined by modeling its distributed scattering as a stochastic process, and the mutual information between scenes and the observed SAR image can be derived. Then, we construct an optimization problem to maximize the mutual information by solving for the optimal beam manipulation scheme of the VR SAR mode. Further optimization of PRF is conducted to obtain a relatively larger swath width and smaller data volume by compromising the resolution of some regions with less information while ensuring there are no azimuth ambiguities. The potential advantages of VR mode are: 1) it simultaneously provides higher resolution for the high-information regions and a larger imaging area than would otherwise be possible in strip map and spotlight SAR modes and 2) it optimizes the efficiency of data acquisition while extracts as much information from scenes as possible. A mathematical model of VR SAR mode with the principle of maximum mutual information is established, and the feasibility and merits of the method are demonstrated through a series of simulations and an equivalent experiment using RADARSAT-1 raw data.

In the companion paper (Zhanget al., 2020), we identified two types of calibration models (CMs) that have been widely used in polarimetric calibration algorithms with distributed targets. An optimal method based on the covariance matching estimation technique (COMET), which we refer to as theOmethod, was used therein but without proof of its optimality. This article supplies the details on parameter estimation using theOmethod and proves that it is optimal in the mean-squared sense: No other estimate has a smaller mean-squared error. For data affected by the Faraday rotation (FR), the feasibility of theOmethod is analyzed. Numerical experiments are presented to compare theOmethod with existing methods and prove its optimality. The differences between theOmethod and another COMET-based calibration method are discussed. Moreover, we prove that estimating the distortion parameters while preserving the orientation angle is impossible, contrary to what is found by Ainsworthet al.(2006).

The interferometric vertical wavenumber (or baseline) has a direct impact on PolinSAR forest height mapping, which must be selected appropriately to acquire optimum inversion performance. In this paper, the key parameters influencing the height estimation precision are considered to simplify the system performance analysis. A PolinSAR performance optimization problem is then established according to the geometrical interpretation of the line coherence model. Finally, the contour map of optimum vertical wavenumber varying with forest height and wave extinction is intuitively provided, from which the system designers can easily determine the optimum baseline for PolinSAR forest height mapping.

Calibration of satellite-borne radiometer is a key issue for quantitative remote sensing. Its accuracy depends on the stability of the calibration source. Because of no atmosphere and biological activity, the Moon surface keeps stable in the long term and may be a good candidate for thermal calibration. Observation of microwave humidity sounder (MHS) onboard the NOAA-18 made measurements of the disk-integrated brightness temperature (TB) of the Moon for the phase angle between -800 and 500. The measurement of NOAA-18 has been studied to validate the TB model of lunar surface. In this article, the near side of the Moon surface is divided into 900 subregions with a span of 60 x 60 in longitude and latitude. By solving 1-D heat conductive equation with the thermophysical parameters validated by the Diviner data of the Lunar Reconnaissance Orbiter (LRO), the temperature profiles of the regolith media in all 900 subregions are obtained. The loss tangents are inversed from the Chang'e-2 (CE-2) 37-GHz microwave TB data at noontime. Employing the fluctuation-dissipation theorem and the Wentzel-Kramer-Brillouin (WKB) approach, the microwave and millimeter-wave TBs of each subregion are simulated. Then, the weighted average TB can be disk-integrated from 900 TBs of all subregions versus the phase angle. These simulations well demonstrate diurnal TB variation and its dependence upon the frequency channels. It is found that the disk-integrated TB of the Moon in MHS channels is sensitive to the full-width at half-maximum (FWHM) of the deep space view (DSV), which is corrected in our simulation, where the Moon is now taken as an extended target, instead of a point-like object. Simulated integrated TBs are compared with the corrected MHS TB data at 89, 157, and 183 GHz. The simulated TB is well consistent with these MHS TB data at 89 and 183 GHz at various phase angles. But the maximum TB of MHS data at 157 GHz is unusually lower than that of 89 GHz. The influence of the loss tangent, emissivity, and the pointing error is analyzed. Some more careful design to observe the Moon TB and technical parameters, especially the FWHM should be well determined. Our model and numerical simulation provides a tool for TB calibration and validation.

The permanent shaded regions (PSRs) at the lunar poles receive no direct solar illumination throughout the year, so their temperatures are extremely low. The PSR is mainly heated by the radiation heat flow and the scattered solar radiation from the sunlit crater wall. The temperature distribution in the PSR and its diurnal and seasonal variations have been calculated using the ray-tracing method, which determines the radiation heat flow and the scattered solar radiation. In this article, the radiation heat flows were calculated by anisotropic emissivity of the PSR, and the scattered solar radiation was calculated using the lunar Lambert model. To conform to the Diviner IR temperature data, the 1-D heat conduction equation was solved with modified heat conductivity (an important parameter of the regolith media). As an example, the daytime and nighttime temperatures in the Hermite-A crater at the North Pole during summer and winter were numerically simulated and were compared with the Diviner IR data. In addition, rocks near the central peak of the crater in the PSR may enhance the nighttime temperature. This was validated by the PSR images captured by the Lunar Reconnaissance Orbiter Camera (LROC), the Miniature Radio Frequency instrument data on the LRO, and the numerical simulations.

To date, several different methods for polarimetric synthetic aperture radar (SAR) calibration with distributed targets have been proposed in the literature. The basic assumptions on the distributed target that are used by these methods are almost identical. Their difference is about the assumptions on noise. In this article, the research shows that the subtle difference between the assumptions on noise leads to two different calibration models (CMs), which is the primary cause of the differences between various methods. According to the used CM, the methods in the literature can be categorized into two groups. Because this article focuses on the CMs, thus the optimal estimator in each group is used for comparison so as to exclude the impacts of different parameter estimation algorithms. The results suggest that neither of the optimal estimators is always superior to the other. In practice, we cannot determine which estimator is better, so we recommend using the mean value of the two optimal estimators (i.e.,$ { \widehat {\boldsymbol \varphi }}^{\star } $) for calibration because it was proved to be (at least) better than the worse one. In the research, the signal-to-noise ratio (SNR) was proved to be a proper indicator for assessing whether${ \widehat {\boldsymbol \varphi }}^{\star } $is reliable. Hence, an estimator for the mean SNR is proposed. In this article, some simulation experiments are used to verify some critical conclusions that we have drawn. The practical use of${ \widehat {\boldsymbol \varphi }}^{\star } $and an assessment of its reliability with the estimated mean SNR are illustrated with DLR E-SAR data from the 2006 AgriSAR campaign.

With the development of coding metasurface antennas, here a generalized SAR modality named variable-resolution (VR) SAR is proposed, which involves diverse radiation pattern and inhomogeneous pulse repetition frequency (PRF) to achieve continuously variable azimuth resolution. Optimization of PRF has been conducted by making resolution of some unimportant details decline with no azimuth ambiguities. There are two potential advantages of VR SAR mode: 1) simultaneously offers the higher resolution for the concerned parts of whole scenes and larger image sizes than would otherwise be possible in stripmap and spotlight SAR modes and 2) relaxes the pressure of data storages and brings a high imaging efficiency. The mathematical model of VR SAR system is built up and feasibilities are demonstrated through a series of simulations and an equivalent experiment based on RADARSAT-1 raw data. This VR SAR opens a new venue for earth observation, space-borne remote sensing and related SAR images processing, heading for agile frequencies, beam pattern and beyond.

A small tilt in the spin axis of the moon over the ecliptic plane causes seasonal incidence variation of solar illumination and, especially, causes significant temperature difference at the polar region. In this article, following the position of the subsolar point, the real-time model of solar illumination incidence over the moon polar region is developed. Based on this model with solving the 1-D heat conduction equation, the seasonal temperature of the lunar surface is obtained and is in agreement with the Diviner infrared (IR) data. Meanwhile, using the fluctuation-dissipation theorem and the Wentzel-Kramers-Brillouin (WKB) approach for lunar regolith media, the seasonal microwave (MW) brightness temperature (TB) is also obtained and validated by Chang'e-2 (CE-2) 37-GHz TB data. These data also show that the lunar surface temperature and the MW TB even in the permanent shaded region (PSR) undergo seasonal variation as well. It might be due to the seasonal thermal radiation on the topographic PSR coming from the sunlit crater walls caused by seasonal temperature variation. The Diviner IR data show that the highest temperature in the Hermite-A crater at the north polar PSR can reach 109 K in summer.

This article proposes a generalized modeling and simulation approach for correlated synthetic aperture radar (SAR) texture based on the Gaussian coherent scatterer model. It is rooted in the physics-based coherent scatterer assumption where each observation in an SAR image is a coherent sum of multiple underlying Gaussian scatterers. The proposal generalizes existing single-point statistical models by allowing the number of scatterers to be a correlated random field. It can also generate the desired spatial correlation texture by stipulating the structure in both the Gaussian scattered field and the number of scatterers. This generalized model is derived theoretically and then validated by both simulations and experiments with SAR data from actual sensors.

Circular polarization ratio (CPR) was regarded as an important index for water ice existence in permanently shadowed region (PSR) of the Moon poles. However, some studies have intuitively described that the double bounce scattering caused by dihedral structure of stone facets may yield high CPR as well. In this paper, a numerical model of the Moon cratered rough surface with volumetric scatterers, e.g. rock-stones, is developed. The bidirectional analytic ray tracing (BART) method is applied to numerically solve high-order scattering of lunar rough surface and volumetric rock-stones. The lunar surface is modeled by Digital Elevation Model (DEM) data from Lunar Reconnaissance Orbiter (LRO) Lunar Orbiter Laser Altimeter (LOLA). It is the first numerical approach to quantitatively analyze how the cratered surface topography, discrete stones and radar local incidence etc. affect and enhance the CPR.

The Moon is a good calibration source for the millimeter-wave radiometer onboard the Feng Yun-4 Millimeter (FY-4M) satellite. The temperature profiles of the lunar surface can be obtained by solving the one-dimensional heat conduction equation with the thermo-physical properties of regolith. Then, the loss tangent is fitting using the Chinese Chang'e-2 (CE-2) 37GHz brightness temperatures (TB) data against TiO2 abundances, which is derived from Clementine five-bands multispectral data. The calculated surface temperatures and the TBs are validated by Diviner infrared data and CE-2 37GHz TB data. With the theoretical analysis of the radiative transfer, the TBs at FY-4M frequencies of lunar center area is obtained for calibration. In addition, the abnormal thermal emission at "cold spots" should be excluded for calibration.

This paper focuses on the generalization ability of model for SAR automatic target recognition (ATR). An object-based similarity evaluation method for MSTAR datasets is proposed at first to show the relationship between classification accuracy and orientation difference between training and test images. It reveals poor orientation generalization ability of traditional methods for orientation interval larger than 10deg. In order to improve the orientation generalization ability, a novel adversarial autoencoder neural networks (AAN) is proposed in this paper. It learns a code-image-code cyclic network by adversarial training for the purpose of generating new samples at different azimuth angles. The learned orientation predictor and classifier is applied to test samples. Proposed network achieved over 86% classification accuracy on 7-type MSTAR datasets when minimum orientation interval is limited to 25deg, and is about 4% higher than baseline model A-ConvNets under the same condition.

TwinSAR-L (Terrain Wide-swath Interferometric L-band SAR mission) is an innovative space-borne bistatic SAR mission for global dynamics, which will be launched in 2020. This paper investigates ionospheric effects on phase and Faraday rotation of interferometry for TwinSAR-L systems. This ionospheric offset arises from different incident angles along each path in inhomogeneous ionosphere. Plus, the inhomogeneity of ionospheric TEC will cause different range delay and defocusing due to dispersion and azimuth shift. The analysis in this paper will be important for TwinSAR-L mission and potential Tandem-L mission in the future.

Terrain surface classification is probably the most common application of polarimetric SAR (PolSAR) data. Methods for PolSAR terrain classification can be divided into either supervised or unsupervised. In this paper, PolSAR image classification algorithms are reviewed from traditional polarimetric methods such as alpha-H-, Freeman-, Yamaguchi-decomposition, to deep learning, and then a general deep learning algorithm is proposed to PolSAR data classification. The suitability and potential of deep convolutional neural network in supervised terrain classification of PolSAR images has been investigated. The results show that deep learning based method can be used for PolSAR terrain classification.

SAR image due to its nature of coherent imaging manifests both deterministic semantic information and speckle-like statistical textures. It is necessary to have a general representation scheme of the semantic-statistical two-layer hierarchy of SAR image so that semantic and textural information can be separated. Inspired by the correlated clutter simulation method proposed by Bustos et al [1]–[2], this paper studies a semantic-statistical convolution scheme to generate a SAR image from a semantic map. For each terrain type, we estimate the intensity distribution and correlated texture model and then generate textures with correlated clutter. The method is tested on actual SAR images of E-SAR data including urban and forest areas and Flevoland AirSAR data with 15 terrains.

This paper presents a method to intercalibrate the brightness temperature (TB) acquired by the Microwave Radiation Imager (MWRI) on Chinese meteorological satellite Fengyun 3C (FY-3C) against the measurements obtained by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) based on an ocean microwave radiative transfer model, and the intercalibration coefficients of FY-3C MWRI are obtained. The results show that the MWRI measurements are underestimated, especially the TB in the low frequency bands, and the calibration bias decreases with the frequency increment. In addition, the calibration of FY-3C MWRI ascending data is much better than that of FY-3C MWRI descending data.

One of Chinese meteorological satellites, Feng Yun-4M (FY-4M), as one the of new generation of geosynchronous series satellites, is planning to upload multichannel millimeter-wave radiometers, e.g., from 50 to 430 GHz. Due to long-period stability and no atmospheric interference, brightness temperature (TB) of the lunar surface can be seen as a good candidate for thermal calibration of FY-4M radiometers. In this paper, the physical temperature profile of lunar regolith media is first derived by resolving 1-D heat transfer equation with validation of the measurements of the Diviner lunar radiometer experiment onboard the lunar reconnaissance orbiter. Then, the loss tangent is fit and validated using the TiO2 abundances, which is derived from Clementine five-band multispectral data and Chinese Chang'e-2 37-GHz TB data. Multichannel TB of a lunar surface region along the moon equator at certain lunar time (noon and midnight) is numerically derived for all FY-4M channels. TB of lunar equator center area (0°N, 0°E) is presented with variation of the lunar local time. These results can be well applied to calibration of FY-4M, with a sustainable error in the range of 1.8 (425 GHz) to 3.8 K (89 GHz).

The anomalous “hot spots” in infrared (IR) bands on lunar surfaces were discovered earlier in the 1960s. It was then found that those “hot” spots in IR turned out to be “cold” spots at night in microwave (MW) bands observations by Chinese CE-1, CE-2 (Chang'e) radiometers. It was intuitively explained by the thermal properties of a large number of rocks over the cratered surface. In this paper, a radiative transfer layering model of rocks over regolith is presented for the quantitative analysis of the anomalous IR “hot” and MW “cold” spots. First, the physical temperature profiles of the rock/regolith media are derived by solving the 1-D heat conduction equation. Brightness temperatures (TB) of these media in both IR and MW are obtained. Due to large inertia of rocks, its temperature is higher than regolith surface at night and causes IR “hot spots.” However, quickly cooled surface at night reverses the temperature profile of the regolith. Due to the MW penetration, warm regolith beneath the surface without rocks can contribute more TB in comparison of “cold spots” of the rocky surface. Based on the thermo-physical and dielectric parameters of layering rock and regolith media, the quantitative results of our radiative transfer model in both IR and MW bands well match the data of Diviner IR and CE-2 MW-37 GHz.

We propose a deep neural networks based method to reconstruct full polarimetric (full-pol) information from single polarimetric (single-pol) SAR data. It consists of two parts: feature extractor which is used to obtain multi-scale multi-layer features of targets in single-pol gray image, and feature translator that converts the geometric features to defined polarimetric feature space. The proposed method is demonstrated on L-band UAVSAR of NASA/JPL images over San Diego, CA, and New Orleans LA, USA. Both qualitative and quantitative results show the reconstructed full-pol images agree well with true full-pol images, the proposed networks have a good spatial robustness. Model-based target decomposition and unsupervised classification can be used directly on constructed full-pol images.

Polarimetric interferometric SAR (PolinSAR) data are contaminated by cross-talk and channel imbalance. To ensure the successful estimation of forest heights from forthcoming PolinSAR campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. In this paper, a triple-factor analysis of cross-talk, channel imbalance and noise of PolinSAR system is conducted to understand the polarimetric system requirements for PolinSAR forest height mapping. A model relationship between forest height estimation error and polarimetric system parameters is established through theoretical analysis. Meanwhile, the numerical relationship between the two is obtained by artificially adding different system errors to simulated SAR images. The experiment results well validate the correctness of our established model relationship. The polarimetric system requirements of PolinSAR for forest height mapping can be provided for system designers according to our established relationship.

Searching for water–ice in the lunar media has been a key issue in the moon explorations. Missions of mini-SAR and mini-RF SAR made compact-pol (polarization) measurements on lunar polar permanent shadowed region (PSR). High circular polarization ratio (CPR) data and a two-layer model were applied to studying if water ice in PSR could be retrieved. However, it has been studied that high CPR is not simply due to the presence of water ice, and the Campbell model is a degenerated half-space model, which confused final inversion. In this letter, using the mini-RF data on the PSR of Hermite-A crater on the north pole, a two-layer model with the Kirchhoff and small perturbation approximations is presented. It takes account of the surface-layering topography, which makes changes of local incidence and polarimetric base rotation. Our results do not support Calla’s conclusions based on the half-space model, suggesting that the inversion of mini-RF is not so straightforward in proving water–ice existence in the PSR media. It points out that the double- and high-order scattering of random volumetric scatters on the lunar surface might play a role, and high-resolution measurements and more elaborate modeling are needed for further studies.

Based on a joint analysis of linear Kelvin wake kinematics and water dispersion relation, the features of several components of ship wake are identified in the spectral domain, such as the “X”-shaped Kelvin wake, the narrow “X”-shaped solitary wave packets, and the cross-shaped turbulent and near-field waves. Alternatively, these components can be separately reconstructed in spatial domain using the inverse Fourier transformation. These relations are verified through numerical simulation of the wakes of a ship moving at different speeds. This wake decomposition is now extended to wake feature analysis of real synthetic aperture radar (SAR) image. It reveals that although the images of ship wake have been modulated by SAR imaging mechanisms in various aspects, their spectral characteristics are closely analogous to that of wake surface elevation. Taking advantage of the loci and shape of the wake spectrum, the transverse wave, the divergent wave, the turbulent wave, and the solitary wave packets can be isolated from the original SAR image with full wake appearance. The reconstructed images of wake components facilitate the further estimation of the direction, speed, length, hull geometry, and propulsion system of the ship. This decomposition can also recover wake components from multiple ship wakes and provide an understanding of their roles on SAR image.

To monitor a space target, 3-D reconstruction from a multiview sequence of the inverse synthetic aperture radar (ISAR) imaging is developed. Scattering of a complex electric-large target, e.g., the ENVISAT satellite model, is numerically calculated, and multiview 2-D ISAR imaging can be simulated. Under the sparse sampling ISAR imaging via compressed sensing, the Kanade-Lucas–Tomasi feature tracker is applied to extraction of target feature points. Then, using the orthographic factorization method, 3-D reconstruction of those feature points is produced. A simple hexagonal frustum is first tested for the feasibility analysis. Two sequences of multiview ISAR imaging, one is the ENVISAT model and another real measurements of a space shuttle, are then presented for 3-D reconstruction. Furthermore, a complex multistructure model of the International Space Station is also studied from multiview ISAR imaging under different sparse sampling rates. All results demonstrate good feasibility of the 3-D reconstruction for those target components, e.g., solar panel and antenna.

Radar range echoes of the UHF radar souder is presented to explore the lunar surface in the permanently shdaowed polar regions (PSR). Based on radiative transfer with the Euler-angles transformation, the temporal Mueller matrix to take account of all scattering mechanisms of undulated rough surface and underlying scatter media is derived. It is applied to invert the dielectric constant of PSR region for water ice detection.

Multi-station inverse synthetic aperture radar (MS-ISAR) imaging mode for on-orbit space target is presented. The MS-ISAR network produces bistatic ISAR images from different radar systems in different locations, and may retrieve more information of on-orbit space target, especially, for applications of target detection and recognition, as well as three dimensional (3-D) reconstruction. Numerical scattering/MS-ISAR imaging of a complex electrically-large target, which is different from simple point-scatterers, are simulated to demonstrate the facilities of multi-station networks, and an example of the Envisat on real orbits is presented.

Compared to quad polarimetry (quad-pol), compact polarimetry (compact-pol) can reduce the system complexity and data volume but sacrifice the retrievable information content. Reconstruction of pseudo quad-pol data from compact-pol has been proposed mostly based on iterative algorithms which make use of the empirically parameterized model with the assumption of reflection symmetry. In this paper, a systemic inverse problem model for quad-pol reconstruction is formulated by developing the linear relationships between the three compact-pol and quad-pol covariance matrices. We then developed a novel Wishart-Bayesian regularized method to solve the inverse problem. The method is verified with Flevoland AirSAR data.

Dielectric inversion of lunar permanently shadowed region (PSR) of moon poles has been studied for estimation of possible water-ice content. The Campbell model was directly applied to mini-SAR data for inversion on the Hermite-A crater region. However, this letter presents quantitative analysis that the lunar surface topography, i.e., surface roughness and slopes, and underlying dielectric media, and so on, can significantly affect this inversion. The model is actually degenerated into a half-space model without topographic account. This letter presents a two-layer model of Kirchhoff-approximation surface/small perturbation approximation subsurface to take account of all these topographic factors for PSR dielectric inversion.

Layover scatterers of tall building structures can be separated by synthetic aperture radar tomography (SAR-tomo). An iterative reweighted L1 minimization (IRL1) has been applied to enhance the sparsity in a tomographic inversion, where the basis pursuit (BP) technique was adopted to search for the solution. However, the IRL1 with BP is highly time-consuming, which may prevent its real application to large-scale data sets. In this letter, we propose the iterative reweighted alternating direction method of multipliers (IR-ADMM) for fast SAR-tomo imaging. We demonstrate and validate the enhanced sparsity and fast convergence of our IR-ADMM algorithm with experiments using both simulated data and TerraSAR-X Stripmap images of tall urban buildings. The experimental results show that compared with conventional IR-BP, the IR-ADMM greatly reduces the computation time without substantial performance degradation.

Compact polarimetry (compact-pol), as an effective polarization system, can reduce the system complexity and data volume in comparison with quad polarimetry (quad-pol). Reconstruction of quad-pol data from compact-pol has been discussed mostly based on iterative algorithms which make use of the empirically parameterized model with the assumption of reflection symmetry of the scatterer. In this letter, a linear relationship between the compact-pol and quad-pol is first derived, and then the Wishart-Bayesian regularized inverse algorithm is developed to reconstruct pseudo quad-pol data from compact-pol. Such problem is solved using the efficient alternating direction method of multipliers to recover the pseudo quad-pol covariance matrix. The reconstruction performance is evaluated by coherence index, in comparison with existing methods.

Imaging distortions induced by each particular rotation of a moving ship are quantitatively investigated through numerical simulations. Two sets of dynamics are considered in the model, namely a ship with pitch, yaw, and roll rotations, and the time evolutions of its wake. To construct high-resolution synthetic aperture radar (SAR) images in spotlight mode, the time-varying scattering from the electrically very large scene is computed by a parallel quasi-stationary algorithm, in which the physical optics (PO) method is used to compute the scattering from the ship, and a PO phase correction of the two-scale model is used to take account of the Doppler effects caused by the wake. Reasonable agreement is obtained when comparisons are made between the simulated and real SAR images. A range of imaging distortions are observed and analyzed, such as the displacement, rotation, stretching/compressing, and broaden/narrow of the ship image. A systematic analysis shows that these distortions can be characterized by four main types of transformations, namely translation, rotation, scaling, and shearing. This paper presents quantitative insights into the data interpretation and signature classification of ship on SAR image.

This paper reformulates the problem of polarimetric incoherent target decomposition as a general image factorization which aims to simultaneously estimate a dictionary of meaningful atom scatterers and their corresponding spatial distribution maps. Both model-based and eigenanalysis-based decompositions can be seen as special cases of image factorization under specific constraints. The inverse problem of image factorization can be converted to an equivalent nonnegative matrix factorization (NMF) problem via redundant coding. It enables a wide range of NMF algorithms with various regularizations to be directly applicable to polarimetric image analysis. The advantage of the proposed image factorization is demonstrated on both synthesized and real data. It also shows that extended applications such as speckle reduction and classification can benefit from the proposed image factorization.

Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy.

This paper presents a three-dimensional (3-D) target reconstruction characterized by its geometric components, i.e. canonical scattering objects. The target is decomposed into some primitive geometries, which are extracted to represent and characterize scattering features. The object parameters are estimated in the frequency domain based on the least square method, and all estimated objects uses the model-based reasoning rules to reconstruct 3-D target. The simulation of a simplified tank model is used to validate the feature extraction and target reconstruction.

High Frequency (HF) radar sounder has been employed for the Mars surface/subsurface exploration. To survey the seasonal water flows on Mars surface, which has been reported, the radar sounder echoes from dry surface or water flowing surface are numerically simulated, respectively. Simulation results show that the radar echoes from water flowing surface is significantly larger than the dry surface, as seasonal change of the surface dielectric property varies. To validate this simulation, some SHARAD data of the Palikir Crater during seasons, which was reported to have canals with flowing liquid saline water during warm seasons, are specifically chosen. Numerical simulation and SHARAD data demonstrate that the radar sounder exploration is a good technology for global survey of possible water flowing on Mars surface during seasons.

Using numerical scattering simulation of the BART (bidirectional analytic ray tracing method), an angular sequence of high-resolution 2-D ISAR (inverse synthetic aperture radar) images of a space target is produced. The Kanade-Lucas-Tomasi (KLT) feature tracker is then adopted for extracting feature points and matching all angularly consecutive ISAR images. 3-D positions of those featured points can be retrieved by the orthographic factorization method (OFM). As a test, a simple hexagonal frustum is used for validation and analysis. Then, two ISAR imaging sequences, one is the simulation of the Envisat satellite model and another real measurements of the space shuttle, are presented to demonstrate 3-D shape information. It shows good feasibility for 3-D status evaluation and pointing control of solar array of a satellites in orbit.

Multi-signal compressed sensing with total variation (MTV-CS) is developed for tomographic inversion of building structure. Incorporating with prior information of the building object, some particularly aligned pixels are combined via the minimization of the object function, as indicated by total variation regularization. A numerical simulation of scattering and SAR imaging of the buildings and the TerraSAR-X imaging data are applied for MTV-CS inversion.

To explore anisotropic information embedded among sub-aperture SAR images, we propose polarimetric sub-aperture singular value decomposition, where polarimetric and anisotropic features are simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigen-analysis-based polarimetric target decomposition, while the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic pattern, where two new parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these new anisotropic parameters can identify specific types of scatterers.

The high-frequency radar sounder is an effective tool for Mars surface/subsurface exploration. To survey the seasonal water flows on the Mars surface, which have been recently reported, the radar sounder echoes from dry surface or water flowing surface are numerically simulated, respectively. The cratered rough surface is first divided into triangulated meshes, and numerical range echoes of the radar sounder from rough surface are calculated using a physical optics approach. Simulation results show that the radar echoes from water flowing surface with high dielectric constant are significantly enhanced, as signatures of seasonal and diurnal variation of surface dielectric properties during the daytime of warm season. To validate these simulation results, two orbits of SHAllow RADar (SHARAD) data, one on daytime of early autumn and another one at night of winter, passing over the Palikir Crater region are specifically chosen. It had been reported that there might be canals with flowing liquid saline water during daytime of warm seasons. Comparison of seasonal and diurnal SHARAD data on the same location can illustrate the enhanced radar echoes likely due to the appearance of surface brines. Quantitative inversion of the surface dielectric constant in warm seasons is also attempted. Numerical simulation of parameterized surface model and SHARAD data demonstrate that the radar sounder exploration is a good technology for the global survey of possible water flowing on the Mars surface during the daytime of warm seasons.

Rapid advances in synthetic aperture radar (SAR) technologies have brought challenges in image interpretation toward the development of new Earth observation applications. Both novel scattering-imaging models and intelligent inversion techniques are required for advanced information retrieval and interpretation of high-resolution multidimension and multimode SAR data. As an example, scene reconstruction attempts to transfer the SAR image to human-understandable representation of man-made targets and natural environment. In this letter, a framework for scene reconstruction is outlined. It includes three key elements: a dictionary of parametric scatterer model, a method for scatterer recognition and parameter estimation, and a method for target reconstruction. A preliminary case is presented, where a simulated 3-D SAR image of a simple target is successfully reconstructed to a solid geometry. It uses a novel surface extension method to connect isolated scatterers to form a complete target geometry.

Deep convolutional neural networks have achieved great success in computer vision and many other areas. They automatically extract translational-invariant spatial features and integrate with neural network-based classifier. This letter investigates the suitability and potential of deep convolutional neural network in supervised classification of polarimetric synthetic aperture radar (POLSAR) images. The multilooked POLSAR data in the format of coherency or covariance matrix is first converted into a normalized 6-D real feature vector. The six-channel real image is then fed into a four-layer convolutional neural network tailored for POLSAR classification. With two cascaded convolutional layers, the designed deep neural network can automatically learn hierarchical polarimetric spatial features from the data. Two experiments are presented using the AIRSAR data of San Francisco, CA, and Flevoland, The Netherlands. Classification result of the San Francisco case shows that slant built-up areas, which are conventionally mixed with vegetated area in polarimetric feature space, can now be successfully distinguished after taking into account spatial features. Quantitative analysis with respect to ground truth information available for the Flevoland test site shows that the proposed method achieves an accuracy of 92.46% in classifying the considered 15 classes. Such results are comparable with the state of the art.

The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.

An iterative alternating direction method of multipliers (ADMM) is proposed for inverse finite-element–boundary-integral (FE–BI) problem with total variation (TV) regularization. The inverse FE–BI fits to a wide class of penetrable sensing applications, where this study specifically targets the problem of radio tomography of asteroid interior structure using orbiting spacecraft. The TV regularizer enforces sparsity on the gradient of reconstructed permittivity, which agrees well with the “piecewise constant” reality of “rocks embedded in soil” scenario and, meanwhile, addresses the inherent ill-posedness. For large-scale asteroid problems, the distributed ADMM algorithm is adapted to solve the linear TV inversion at each iteration. The 2-D inversion is validated with the Fresnel Institute measurement data. Simulated cases of asteroid internal imaging are also presented. The proposed iterative ADMM can be also applied to similar penetrable imaging applications.

In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

In this paper, a new All-Convolutional Networks (A-ConvNets) is proposed and applied to Moving and Stationary Target Acquisition and Recognition (MSTAR) data. Conventional deep learning algorithms, especially the deep convolutional networks (ConvNets) have achieved many success state-of-art results. However, directly applying ConvNets to SAR data will yield severe overfitting because of limited data availability. The proposed A-ConvNets can significantly reduce the number of free parameters and the degree of overfitting. Average accuracy of 99.1% on classification of 10-class targets was obtained by applying A-ConvNets to MSTAR datasets.

Man-made building objects mostly with vertical wall structures may present distinct scattering patterns, e.g., wall/roof upfront scattering, wall-ground double scattering, etc., along the range dimension in high-resolution synthetic aperture radar (SAR) images. In this letter, a 1-D detector, referred to as the “range detector,” is presented for building detection, which operates only along the range direction. Experiments show that this range detector can effectively detect and extract the footprint of the illuminated wall of a cuboid building, with which the outline of the building image can be captured by marching the footprint toward radar. This approach is applied to an airborne Pi-SAR image of Sendai, Japan, and more than 80% of the buildings can be identified. The building height and length are also estimated, and the errors are found around 4-5 m based on optical image.

As ground truth to utilize the surface temperature measurements from the Diviner Lunar IR Radiometer and the subsurface thermal properties from the Apollo heat-flow probes, we create a forward model to predict brightness temperatures (Tbs) from lunar regolith media in the microwave (MW) spectrum. These models can be then directly compared with and matched to the data from the MW radiometers flown aboard the Chang'e 1 and 2 (CE-1 and CE-2) missions. Based on an MW radiative transfer model and the least-mean-square method, the effective surface reflectivity and absorption coefficient of the lunar regolith are retrieved from multichannel MW Tbs. The effective complex dielectric constant of the lunar regolith as a function of the depth at different frequency channels is derived. Meanwhile, we find that the maximum penetration depth of the MW radiation at the Apollo 15 site ranges from about 30 cm at 37.0 GHz to 230 cm at 3.0 GHz and from 30 cm at 37.0 GHz to 560 cm at 3.0 GHz in the equatorial highlands, which are much lower than the previous results that were simply derived from FeO and TiO2abundance.

Simulation of inverse synthetic aperture radar (ISAR) imaging of a space target and reconstruction under sparse sampling via compressed sensing (CS) are developed. The numerical bidirectional analytic ray tracing (BART) method is employed to compute the polarized scattering from an electrically large target. With multiorbit and multistation imaging modes, 2-D and 3-D ISAR images are acquired, leading to information retrieval of the space target, such as shape, structure, attitude, etc. CS is introduced into the reconstruction of ISAR images under sparse sampling. As an example, the models of the Aura satellite and the X-37B orbital test vehicle are presented for ISAR imaging and reconstruction.

Spaceborne high-resolution synthetic aperture radar (SAR) is a potential powerful tool for rainfall pattern and intensity observations over the sea surface. However, many interesting rain-related phenomena revealed by SAR images are still not fully understood due to poor theoretical modeling of the rain–wind–wave interactions. This paper attempts to develop a physics-based radiative transfer model to capture the scattering behavior of rainfall over a rough sea surface. Raindrops are modeled as Rayleigh scattering nonspherical particles, whereas the rain-induced rough surface is described by the Log-Gaussian ring-wave spectrum. The model is validated against both empirical models and measurements. A case study of collocated Envisat ASAR data and NEXRAD rain data is presented to demonstrate the performance of the newly developed model. Finally, numerical simulation results suggest that rain-related scattering becomes significant as compared with wind-related scattering when the frequency is above C-band, whereas the raindrop volumetric scattering becomes significant above X-band.

The main objective of the Chinese Chang'E-3 (CE-3) lunar satellite is to achieve soft-landing and roving exploration on the lunar surface. A multibeam radar in the lunar lander is implemented to measure the echoes from the lunar rough surface during its descending and to derive the speed of the lander. In this paper, numerical simulation of multiangular radar echoes and speed inversions from Doppler frequency are presented. An area of the Lunar Sinus Iridum bay, as landing site, is specifically selected. The rough surface described with the real DTM data is first divided into triangular patches for numerical Kirchhoff approximation calculation. The radar echoes of multiangular radar beams of CE-3 during the landing are numerically simulated. The echo phase and the Doppler frequency are then derived to obtain the vertical speed.

As a second generation of China's polar meteorological satellite, the satellites of FY-3A and FY-3B launched in 2008 and 2010, respectively, carried 11 sensors, including microwave sensors such as microwave temperature sounder (MWTS), microwave humidity sounder (MWHS) and microwave radiometer imager (MWRI) etc. To fuse the MWRI and AMSR-E data and promote MWRI applicability, brightness temperature (Tb) of multi-channel MWRI are first simulated based on a radiative transfer (RT) model, and the difference between the technical features of MWRI and AMSR-E for Tbobservations, correspondingly, are analyzed. Data validation of MWRI and inter-comparison with AMSR-E data are discussed for monitoring drought, precipitation and flooding happened during the periods. It lays applicability of MWRI in future operational services.

An approach of multi-mutual information (M-MI) is presented for change detection and evaluation of building damages after an earthquake. Fusion of very high resolution pre-event optical and postevent synthetic aperture radar (SAR) images becomes feasible for timely evaluation of earthquake losses. Based on the geometric parameters extracted from an optical pre-event image, SAR images of rectangular building objects, i.e., nondamaged or damaged, are first numerically simulated by our mapping and projection approach and are then, using M-MI, applied to similarity analysis with the real postevent SAR image. Three models of building damages, i.e., collapsed, subsided, and deformed, are proposed for classifying mutual information (MI). The M-MI, including normalized MI (NMI), gradient MI (GMI), and regional MI (RMI), are all applied and compared for MI change detection of building damages. Based on the maximum, mean value, and height deviation of NMI, GMI, and RMI, the building damages can be detected and evaluated. As an example, the Ikonos pre-event and GeoEye postevent optical images and COSMO-SkyMed and Radarsat-2 postevent SAR images during the 2010 Haiti earthquake are applied in this M-MI experiment.

A theoretical model of stratified media with non-spherical scatterers and rough interfaces for numerical simulation of polarimetric radar pulse echoes is developed. The time domain Mueller matrix solution derived from vector radiative transfer formulations contains seven scattering mechanisms of the stratified media: surface scattering from the rough top and bottom interfaces, volumetric scattering from random non-spherical scatterers, and their multi-interactions. Temporal characteristics and image features of the polarimetric echo profile as functional dependences on the model parameters, such as the layered structure, thickness and dielectric properties etc., are numerically simulated. Polarimetric pulse echoes may reveal internal structure and other useful information of the layered media, and demonstrate a potential new way to explore the lunar regolith layers and monitor the landslides, etc.

In this paper, interferometric synthetic aperture radar (InSAR) data are utilized to extract the forestry damage information caused by typhoon over Tomakomai, Hokkaido, Japan. By computing the interferograms of two single-pass X-band SAR data before and after the typhoon, the change of their heights could be detected even the fallen tress were not cleared, and therefore, the damage level can then be evaluated. The results show that the main damaged areas detected by InSAR analysis are in agreement with the ground truth data.

On behalf of the Technical Program Committee (TPC), we would like to welcome you to the IGARSS 2011 in Vancouver, Canada, one of the most beautiful cities in the world, and to express my appreciation to the International TPC and AdCom members for their valuable contributions to the symposium.

To monitor debris flows and landslides, geologic surveying has been usually implemented to ascertain where these natural hazards might happen. These traditional observations at discrete sites are very restrictive in both temporal and spatial scales, and cannot make accurate and timely decision for early warning of geologic disaster occurrences. In this letter, very high frequency (VHF; ~100 MHz) pulsed radar is proposed as a monitoring tool to probe the layering land media. Due to large penetration depth of VHF radar on the order of tens of meters, radar echoes can detect the change of water content underneath ground surface, which is an essential stimulator to cause the debris flow and landslides. A model of layering land media embedded by random scatterers (stone or water) with randomly rough interfaces is presented, and polarimetric radar range profiles from underground structures under different situations are numerically simulated. Results show that distinct features in radar range profiles can be directly attributed to underground water content change and/or water distribution. The proposed VHF radar seems promising for early warning of geologic hazards. The differences of radar images between the normal day and warning days, e.g., after severe storm, can be used to predict potential occurrence of debris flow or landslides.

Numerical simulations of polarized scattering, 3-D imaging, and profile reconstruction of a complex-shaped electric-large target above a rough surface are developed. The bidirectional analytic ray tracing method is first applied to calculation of polarized scattering from volumetric target and underlying surface. By using the step-frequency radar working in downward-looking spotlight mode and moving within a 2-D circular arc aperture, a 3-D matrix of backscattering fields in both the amplitude and phase is obtained. The 3-D fast Fourier transform algorithm is adopted for uniformly resampling data, which are acquired by interpolating the collected uniformly sampling backscattering fields to quickly form a focused image. Automatic reconstruction of the target is then well demonstrated. As validation and comparison, the scattering fields are also computed and compared using widely accepted software FEKO based on physical optics. The technique of imaging and reconstruction for a 3-D complex-shaped perfect electric conductor electric-large target, such as a tank-like object, is presented.

In China's first lunar exploration project, Chang-E 1 (CE-1), a multi-channel (3.0, 7.8, 19.35 and 37GHz) microwave radiometer in passive microwave remote sensing, was first aboard the satellite, with the purpose of measuring microwave brightness temperature (Tb) from lunar surface and surveying the global distribution of lunar regolith layer thickness. In this paper, some works on this mission, including the microwave emission modeling and Tb simulation of lunar surface media, CE-1 data validation and retrieval of lunar regolith layer thickness from multi-channel CE-1 Tb data, and evaluation of global inventory of Helium-3 in lunar regolith, are reported.

Numerical study of radar echoes from the targets in environmental clutters has been of great interest in many applications. In this paper, the bidirectional analytic ray tracing (BART) method for composite scattering from three-dimensional (3D) electrically large complex target above a randomly rough surface is reported. Analytic tracing of polygon ray tubes in bidirectional tracing is developed to precisely calculate the illumination and shadowing of facets, which exempt large patches of the target from any finer meshing. It significantly reduces the complexity relevant to the target electric-size. Numerical examples of angularly composite scattering from a three-dimensional electrically large, e.g., a ship-like target over a randomly rough surface are presented and discussed.

The purpose of this study is to extract the forests destroyed by typhoons and to quantitatively estimate the damage levels by using high-resolution polarimetric synthetic aperture radar (SAR) data. The study area is located in Tomakomai, Hokkaido, Japan [1]–[4]. Two sets of data were acquired before and after the typhoon by the L-band airborne Pi-SAR (Polarimetric-interferometric SAR) with 3m × 3m resolution (4-look in azimuth direction). It was found that the values of RCS (Radar Cross Section) averaged over the whole image after the typhoon damage changed by −0.47 dB, 0.05 dB, and 0.64 dB at HH-, HV-, and VV-polarization respectively in comparison with those before the damage. To fully utilize the data, a scattering model of the linear combination of the cross- and co-polarization RCS changes was developed to estimate the damage levels. Similar analytical approaches were also applied using the three-component decomposition analysis. The changes in RCS of double-, volume- and surfacescattering mechanisms after the damage were respectively 27.5 dB, −0.20 dB and −20.3 dB. Finally, by comparing the results with the ground survey data, the accuracies of 64.1% and 77.7% were obtained for the RCS and decomposition analyses respectively.

In China's first lunar exploration project, Chang-E 1, the multichannel (3.0, 7.8, 19.35, 37 GHz) microwave radiometers were aboard the satellite, with the purpose of measuring microwave brightness temperature from the lunar surface and surveying the global distribution of lunar regolith layer thickness, and global evaluation of3He content. To analyze the modeling of microwave radiative transfer from three-layered media of the lunar surface, some factors, such as the cratered lunar surface roughness and scattering of regolith particulate medium with temperature profile, are discussed. The three-layer model makes the predominance of the parameters, such as the regolith layer thickness and stratified structures, to be studied.

During recent decades, China has successfully launched several programs of satellite-borne remote sensing, including the meteorological Feng-Yun (FY, “wind cloud”) series and oceanic Hai-Yang (HY, “ocean”) series, in broad spectra, i.e., optical, infrared, and microwave. Since the initiation from the early 1970s, a total of nine meteorological satellites, FY series, including five polar-orbit satellites and four geostationary satellites, have been successfully launched. Chinese FY has become an important component of global meteorological satellites systems and continues to maintain long-term stable operations of both polar and stationary meteorological satellites. Later in 2002, China's HY-1, an oceanic color satellite, was launched and is now in good operation. The successive HY-2 and HY-3 of the HY series, including microwave sensors, are also on schedule. Relevant basic research, application, and operational service of satellite-borne remote sensing and Earth observation have been well implemented. In this paper, a brief overview of Chinese satellite-borne remote sensing, FY and HY series, is presented, and some progress is introduced.

A method of two-threshold expectation maximum and Markov random field is presented to automatic detection of terrain surface changes after the Wenchuan earthquake, on May 12, 2008, using multitemporal ALOS PALSAR images. As an example in the Beichuan area, three kinds of terrain surface changes, i.e., scattering enhanced, reduced, and no-changed, are automatically detected and classified. By using the tool of Google Earth, the surface change situation after the earthquake can be shown in multiazimuth views as an animated cartoon. The detection and classification are also compared with optical photographs.

Based on the statistics of the lunar cratered terrain, e.g. population, dimension and shape of craters, the terrain feature of cratered lunar surface is numerically generated. According to inhomogeneous distribution of the lunar surface slope, the triangulated irregular network is employed to make the digital elevation of lunar surface model. The Kirchhoff approximation of rough surface scattering is then applied to simulation of lunar surface scattering. The synthetic aperture radar (SAR) image for comprehensive cratered lunar surfaces is numerically generated. Making use of the digital elevation and Clementine UVVIS data at Apollo 15 landing site as the ground truth, an SAR image at Apollo 15 landing site is simulated.

Employing three-dimensional mapping and projection algorithm (MPA), imaging simulation of bistatic SAR (BISAR) observation over complex scenario is developed. Based on the explicit expression of point target response of stripmap BISAR imaging, raw data is efficiently generated from the scattering map pre-calculated by MPA. Some examples of BISAR image simulation are studied. Polarimetric characteristics of BISAR image are then discussed. A transform of unified bistatic polar bases for BISAR image is proposed. Analysis of simulated images shows that the redefined parameters by the unified bistatic polar bases transform well describe different scattering mechanisms in BISAR imaging. It provides a primary tool for BISAR image interpretation and terrain classification.

Polarimetric synthetic aperture radar (SAR) images describe objects via polarization synthesis and present much richer information than monopolarized SAR images. Furthermore, multidirectional flights of polarimetric SAR can see 3-D stereo objects in different angles. The height and the location of the 3-D objects can be retrieved from the polarimetric SAR images in multidirectional flights. This letter presents a tractable approach for the reconstruction of 3-D stereo buildings from two airborne PI-SAR images taken from opposite directions (at X-band with a spatial resolution of 1.5 m).

Employing the 3-D mapping and projection algorithm (MPA), an imaging simulation of bistatic synthetic aperture radar (BISAR) observation over a complex scenario is developed. Based on the explicit expression of the point target response of stripmap BISAR imaging, raw data are efficiently generated from the scattering map precalculated by MPA. Some examples of BISAR image simulation are studied. The polarimetric characteristics of a BISAR image are then discussed. It is found that some typical polarimetric parameters such as Cloude's alpha, beta and gamma and might become unable to describe the scattering mechanism under bistatic observation. A transform of unified bistatic polar bases for a BISAR image is proposed. The parameters alpha, beta and gamma and are modified to retain the property of orientation independence in the bistatic circumstance. Analysis of simulated images shows that the redefined alpha, beta and gamma and after the unified bistatic polar bases transform well describe different scattering mechanisms in BISAR imaging. It provides a primary tool for BISAR image interpretation and terrain classification.

Spaceborne microwave observation of subcanopy and subsurface requires the SAR (polarimetric synthetic aperture radar) technology at lower microwave frequencies, such as P band. However, SAR observation at P band is remarkably influenced by Faraday rotation (FR) effect through ionosphere. An example in this paper illustrates why the measured polarimetric data with FR at P band cannot be directly applied to terrain surface classification. We further present that the parameters u, v, H, α, A for terrain surface classification derived from the polarimetric data without FR, which are recovered from the data with FR, can be applied to the surface classification, even there is a ±π/2 ambiguity error unresolved. Based on gradual change of FR degree along geographical location, a method to eliminate the ±π/2 ambiguity error is designed. Thus, the polarimetric scattering vector and Mueller matrix without FR and ±π/2 ambiguity can be fully inverted from the measured polarimetric data with FR.

In this paper, an approach to the automatic reconstruction of 3D simple building objects from multi-aspect metric-resolution SAR images is proposed. The edge detector of constant false alarm rate (CFAR) and a parallel Hough transform technique are first employed to extract the parallelogram-like image of the building walls in SAR image. A set of probability density functions is presented to describe the extracted random wall-images and their multi-aspect coherence. Then the maximum-likelihood estimation of object is derived from its multi-aspect object-images. A hybrid priority criterion is defined to evaluate the reliability of reconstruction result, based on which, an automatic reconstruction algorithm is further devised to match object-images of different aspects and finally reconstruct the building objects. Four-aspect Pi-SAR images over Sendai, Japan are adopted for reconstruction. The results show the fidelity of the whole process chain and the feasibility of 3D objects automatic reconstruction from multi-aspect SAR images.

This paper presents a hybrid iterative algorithm of analytic Kirchhoff Approximation (KA) and numerical method of moment (MoM) for scattering computation from a three-dimensional (3D) perfect conducting target above a randomly rough surface. The coupling integral equations (IEs) are derived based on the Green’s function and the boundary conditions. The MoM with the Conjugate Gradient (CG) approach is used to solve the target’s IE, and the KA is applied to scattering from the rough surface. The coupling iteration takes account the interactions between the target and the underlying rough surface. Convergence of the hybrid KA-MoM algorithm is numerically validated. Since is only one numerical integral of induced current on the target performed by KA computation, much memory and computation time is reduced. Bistatic scattering from a PEC cubic or spheroid target above a Gaussian rough surface are numerically simulated.

Spaceborne microwave observation of subcanopy and subsurface requires the polarimetric synthetic aperture radar (SAR) technology at lower microwave frequencies, such as P-band. However, SAR observation at P-band is remarkably influenced by the Faraday rotation (FR) effect through the ionosphere. An example in this paper illustrates why the measured polarimetric data with FR at P-band cannot be directly applied to terrain surface classification. We further present that the parameters u, nu, H, alpha, A for terrain surface classification derived from the polarimetric data without FR, which are recovered from the data with FR, can be applied to the surface classification, there is a plusmnpi/2 ambiguity error unresolved. Based on gradual change of the FR degree along a geographical location, a method to eliminate the plusmnpi/2 ambiguity error is designed. Thus, the polarimetric scattering vector and Mueller matrix without FR and plusmnpi/2 ambiguity can be fully inverted from the measured polarimetric data with FR

Reconstruction of 3-D objects from multiaspect high- resolution synthetic aperture radar (SAR) images is of great importance for SAR technology applications. In this paper, simple building objects are modeled as cuboids, and an approach for automatic reconstruction of 3-D building objects from multiaspect SAR images in meter resolution is developed. The edge detector of constant false alarm rate and a Hough transform technique for parallel line segment pairs are first employed to extract the parallelogram-like image of the building walls in SAR images. A set of probability density functions is presented to describe the object images and their multiaspect coherence. The maximum-likelihood estimation of an object is then derived from its multiaspect object images. A hybrid priority criterion is defined to evaluate the reliability of the reconstruction result. An automatic reconstruction algorithm is further developed to match object images of different aspects and, finally, to reconstruct the building objects. Besides, an iterative method is proposed for the coregistration of multiaspect building images. Four-aspect simulated images of a virtual scene and four-aspect Pi-SAR images over the campus of Tohoku University, Japan, are investigated. Reconstruction of building objects from their multiaspect images shows the fidelity of the whole process chain and the feasibility of 3-D objects automatic reconstruction from multiaspect SAR images. At last, a practical application that is based on spaceborne meter-resolution SAR is proposed.

A hybrid algorithm, combining analytic Kirchhoff approximation (KA) and numerical method of moments (MoMs), is developed to solve the coupling electric-field integral equations (EFIEs) of scattering from a perfect electric conducting (PEC) object above a randomly rough PEC surface under TE-polarized plane-wave incidence. The MoM with the conjugate gradient approach is used to first solve the EFIE of the object. The surface fields on the rough surface are analytically expressed using the KA method, and large memory and computations for those fields are greatly reduced. An iterative approach of the surface fields induced on both object and rough surface is then utilized to take into account interactions between the object and underlying rough surface. Convergence of this hybrid algorithm is numerically validated. Making use of Monte Carlo realization, bistatic scattering from a 2-D PEC cylindrical object above a PEC rough surface is well simulated by this hybrid KA-MoM algorithm

Deorienation theory of polarimetric scattering targets is developed, which transforms the scattering vector of spatially oriented targets into a certain status with minimization of cross polarization. A new set of the parameters u,v,w,Psiis defined to describe and classify different terrain surfaces. Based on the vector radiative transfer (VRT) model of non-spherical particles above a rough surface, numerical simulations illustrate the parameters u,v,w,Psiand the entropy H. These parameters are applied to the unsupervised classification in polarimetric images. The terrain surfaces of polarimetric SIR-C and airborne SAR images are classified and orientation-analyzed.

The difference field RCS (d-RCS) has been defined to analyze the scattering from the target above a rough surface. The electric field integral equations (EFIEs) of the difference induced current on the rough surface, the induced electric current and magnetic current on the dielectric target under a TE wave incidence are derived. A small portion of the rough surface towards the target along the specular direction is taken to compute the scattering contribution from the rough surface towards the target, which improves the computation speed. A numerical iterative approach is developed to solve the EFIEs and bistatic d-RCS. The surface length for iterations is dependent on the scattering angle and discussed for comparison with Johnson's method. Using the Monte-Carlo method to generate the P-M (Pierson-Morkowitz) ocean-like rough surface, bistatic d-RCS of the dielectric target, e.g. a cylinder or a square column, above the rough surface is numerically simulated. The induced electric and magnetic currents on the dielectric target, and the difference induced current on the rough surface are numerically discussed.

A novel fast algorithm of polarimetric image simulation for SAR observation over comprehensive terrain scene is developed based on the mapping and projection principles. It incorporates penetrable and impenetrable objects, volumetric and surface scatterers in the imaging space with the extinction, attenuation, shadowing and multiple scattering effects. Scattering of the vegetation canopy is modeled as a layer of random non-spherical particles by using the vector radiative transfer model. Scattering from the ground surface and building objects is calculated by using the IEM rough surface model. As an example, the polarimetric SAR images for a virtual terrain scene, composed by tree canopies, farmland, buildings, rough land surface, hills and rivers, are simulated.

Under a polarized pulse wave incidence, the temporal Mueller matrix solution from vector radiative transfer (VRT) equation for a layer of nonspherical particles above randomly rough surface is constructed. The system response function based on the Mueller matrix solution is developed, which takes into account the scattering intensity matrix of the canopy, attenuation coefficient matrix through the canopy, scattering intensity matrix of underlying ground surface, and echoes time delay. This system response model preserves consistence with the Mueller matrix solution. To evaluate the system response function from the wave profiles of the received pulse echoes, an adaptive nonlinear estimation method (ALM) is proposed. When the pulse echoes are received, it yields the system response function, i.e., four system parameters. These system parameters are used to invert multiparameters of the vegetation canopy and underlying rough surface, which include the canopy depth, scatterers size, orientation, density and dielectric constant, and the surface roughness and dielectric constant. Numerical examples show good performance of our method as a tractable approach for multiparameters inversion. Potential application and some issues of multiparameters inversion are discussed. An envisaged sensor and platform for practical realization is proposed

A novel approach to polarimetric image simulation for synthetic aperture radar (SAR) observation over comprehensive terrain scenes is developed based on mapping and projection principles. It incorporates penetrable and impenetrable objects, volumetric and surface scatterers in the imaging space with extinction, attenuation, shadowing, and multiple-scattering effects. Scattering of the vegetation canopy is modeled as a layer of random nonspherical particles by using the vector radiative transfer model, and scattering from the ground surface and building objects is calculated by using the integral equation method. As an example, polarimetric SAR images at L-band and C-band and the different spatial resolutions for a virtual terrain scene composed of tree canopies, farmland, buildings, rough land surface, hills, and rivers are simulated. The imaging simulation results demonstrate the feasibility of the mapping and projection approach and the potential utilities of SAR imaging simulation

The difference field radar cross section (d-RCS) has been defined to analyze the scattering from the target above a rough surface, which takes account of scattering from the target and multiinteractions of the target and underlying rough surface. The d-RCS removes the effect of the finite illuminated surface length under the tapered wave incidence. In this paper, the electric field integral equations (EFIEs) of the difference-induced current J/sub sd/ on the rough surface and the induced current J/sub o/ on the target are derived. A small section of rough surface toward the target in the specular direction is taken to speed up computation of the scattering contribution E/sub s0/ from the moderate rough surface to the target. Then, an iterative approach is developed to solve the EFIEs of the induced currents, directly, and yields the bistatic d-RCS. A finite rough surface length for numerical iteration is taken, corresponding to the dependence on the maximum scattering angle. Using the Monte Carlo method to generate rough surface, the bistatic d-RCS of the target, e.g., a cylinder or a square column, above a Pierson-Morkowitz rough surface is numerically simulated. The induced currents on the target and the d-RCS are discussed, and compared with the case of the target in free-space.

Deorientation theory of polarimetric scattering targets is presented. Using a transformation of the target scattering vector, the target orientation is turned to a certain fixed state and polarimetric scattering of the transformed scattering vector shows the prominence of the generic characteristics of the target. A new set of parameters u, v, w, /spl psi/ is defined based on a deorientation of the target scattering vector. Numerical simulation of polarimetric scattering of nonspherical particles illustrates the meanings of the parameters u, v, w, /spl psi/ and the entropy H. An unsupervised classification scheme of the terrain surfaces is developed, which classifies the terrain surfaces using the set of u., v, H, and analyzes the orientation distribution of each class based on deorientation angle /spl psi/. As examples, a SIR-C polarimetric image over China's Guangdong Hui-Yang district is classified into eight classes and a AirSAR polarimetric image over Canada's Boreal district is orientation-analyzed using our approach of deorientation and four parameters u, v, /spl psi/, and H.

The time-dependent Mueller matrix solution of vector radiative transfer for stratified random media of nonspherical scatterers is presented. Copolarized and cross-polarized bistatic scattering for a polarized pulse incidence are numerically simulated. Numerical results well demonstrate volumetric and surface scattering mechanism and depict the fraction distribution of random scatterers of stratified random media. The peak tails in polarized echoes due to wave reflections from the underlying surface can be identified. Its copolarized peaks in the specular direction are employed for simultaneous retrievals of the underlying surface roughness and moisture with the presence of stratified vegetation canopy.

To solve a three-dimensional vector radiative transfer (3-D-VRT) equation for the model of spatially inhomogeneous scatter media, the finite enclosure of the scatter media is geometrically divided, in both the vertical z and horizontal (x,y) directions, to form very thin multiboxes. The zeroth-order emission, first-order Mueller matrix of each thin box, and an iterative approach of high-order radiative transfer are applied to deriving high-order scattering and emission of whole inhomogeneous scatter media. Numerical results of polarized brightness temperature at microwave frequency from an inhomogeneous scatter model such as vegetation canopy are calculated and discussed.

The domain decomposition method (DDM) and finite-element method (FEM) are developed for numerical solution of bistatic scattering from the composite model of a ship on and a target above a two-dimensional (2-D) randomly rough sea surface under an electromagnetic (EM) wave incidence at low grazing angle. The coupling boundary conditions on the interface between two adjacent subdomains are derived when the conformal perfectly matched layer is used as the truncation boundary of the FEM, and the final coupling matrices are obtained by using the inward-looking approach. Because the computational domain with several millions of unknowns can be solved on a personal computer, our FEM-DDM method is powerful for scattering simulation of a very large-scale rough surface with targets presence. In addition to reduction of the memory storage, the superiority of this method in computing time over the conventional FEM is also demonstrated. Our codes are examined by the FEM without DDM, the forward-backward method (FBM), and generalized FBM for some simple cases. Numerical simulations of bistatic scattering from a comprehensive model of a ship on and a target above the 2-D randomly rough perfectly conducting sea surface in large electric scale are obtained, and its functional dependence on many physical parameters of the targets and oceanic status are discussed.

The foam covered ocean surface is treated as densely packed air bubbles coated with thin layers of seawater. Two geometric structures are considered. The first is spherical air bubbles in FCC structure. The second one is Lord Kelvin's tetrakaidecahedron with close to minimal contact surface area. We apply Monte Carlo simulations of solutions of Maxwell's equations to calculate the absorption, scattering and extinction coefficients of each layer at 10.8 GHz and 36.5 GHz. These quantities are then used in dense media radiative transfer theory to calculate the microwave emissivity. Results of emissivities for both horizontal polarization and vertical polarizations at 10.8 GHz and 36.5 GHz are illustrated.

The Mueller matrix solution and eigen-analysis of the coherency matrix for completely polarimetric scattering have been applied to analysis of the SAR (synthetic aperture radar) imagery. Usually, the polarization index is defined as a parameter to classify the difference between co-polarized scattering signatures from the terrain surfaces. In this paper, the eigen-values of the coherency matrix and information entropy are derived to directly relate with co-polarized and cross-polarized indexes. Thus, it combines the Mueller matrix simulation, the information entropy of the coherence matrix, and two polarization indexes to yield an overall theory for quantitative understanding of the SAR imagery. This theory is applied to the AirSAR images.

Polarimetric scattering from inhomogeneous random media of non-spherical scatterers under a pulse incidence is studied. The time-dependent Muller matrix solution of vector radiative transfer for layering random media is derived. Co-polarized and cross-polarized bistatic and backscattering are numerically calculated. The shape and intensity of polarized echoes well depict the inhomogeneous fraction profile of random scatterers. Its functional dependence upon the fraction profile, layering thickness, and other parameters are discussed. This technique is applicable to the reconstruction of inhomogeneous fraction profile and inversion of the media thickness.

The first and second-order iterative solutions of the scalar radiative transfer equation for a layer of random non-spherical particles are derived. An iterative method for retrievals of the Legendre coefficients of the phase function linking with the physical parameters of small spheroids is developed. Using two measurements of azimuth distribution of bistatic scattering, the dielectric constant of random small spheroids and number of particles per unit area are iteratively inverted.

To measure fully polarimetric scattering from random chiral small spheroids, the 2 × 2 dimensional (2 × 2-D) complex scattering amplitude matrix of randomly oriented, chiral small spheroid is derived. Polarimetric scattering from a bounded layer of non-uniformly oriented, chirally-active small spheroids in the Mueller matrix solution is obtained. Co-polarized and cross-polarized backscattering and polarization degree for any polarized incidence (χ, ψ) are numerically calculated. Transmitting of coherent Stokes parameters through the layer are also discussed. Either non-uniform orientation or chirality can yield non-diagonal extinction matrix {ie130-1} and full eigenmatrix Ē due to the fact of forward depolarized-scattering functions 〈f 〉, 〈f 〉 ≠ 0. Comparisons of fully polarimetric scattering from the chiral and achiral particulate media demonstrate the chirality effect on wave scattering and transmitting.

The Mueller matrix solution and eigenanalysis of the coherency matrix for completely polarimetric scattering have been applied to the analysis of synthetic aperture radar (SAR) imagery. Copolarized and cross-polarized backscattering for any polarized incidence can be obtained. The polarization index is usually defined as a parameter to classify the difference between polarized scattering signatures from the terrain surfaces. The eigenvalues of the coherency matrix and information entropy are derived to directly relate with measurements of the copolarized and cross-polarized indexes. Thus, it combines the Mueller matrix simulation, the information entropy of the coherence matrix, and two polarization indexes together and yields a quantitative evaluation for surface classification in the SAR imagery. This theory is applied to analysis of the AirSAR images and field measurements.

Employing the model of a layer of continuous random medium with an underlying rough surface, the bistatic scattering and backscattering coefficients are calculated. By using the reciprocity, the emissivity is then calculated. Numerical results simulate the temporal variations of complementary backscattering and emissivity of the vegetation canopies. Theoretical results are compared with the measurements of active and passive remote sensing of several vegetation canopies. The correlated characteristics of microwave active and passive remote sensing are discussed.

By using the first-order iterative solution of vector radiative transfer in small albedo for a layer of nonuniformly-oriented, random, nonspherical scatterers, the Mueller matrix and coherency matrix are obtained. Eigenanalysis of the coherency matrix is numerically calculated. The eigenanalysis of the coherency matrix is proposed as a better method for identifying physical scattering mechanisms than direct inspection of the Mueller matrix. The functional dependence of polarimetric scattering is also discussed.>