Orthogonal Time Frequency Space (OTFS) enables reliable communication in high-speed mobility scenarios, making it ideal for Low Earth Orbit (LEO) satellite communication. Furthermore sparse Code Multiple Access (SCMA) supports massive connection in uplink mobile communications. This paper proposes an OTFS-SCMA scheme for LEO satellite communications and the corresponding cross domain detection algorithm. At the transmitter, users are grouped, and a practical codebook is employed. At the receiver, cross-domain detection is utilized to obtain initial estimates, which are then refined using the Message Passing Algorithm (MPA) for optimized detection. Comparative analysis with other baseline schemes demonstrates the performance gain.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

Quad polarimetric (quad-pol) Synthetic Aperture Radar (SAR) has unique advantages to record the detailed backscattering information, which significantly enhances the interpretation quality of objects. However, the quad-pol SAR system’s intricate nature imposes constraints on its swath coverage and spatial resolution. A promising solution to this challenge lies in the recovery of quad-pol information from Compact-Polarimetric (CP) SAR data. In this paper, recovery methods based on the convolutional neural network (CNN) are introduced, for both single-look complex (SLC) and multi-look complex (MLC) SAR data. It also presents a comparative performance analysis of these recovery techniques across two data types, emphasizing the feasibility and efficiency of two approaches in overcoming the limitations of quad-pol SAR systems while maintaining high data quality.

Multibaseline interferometric synthetic aperture radar (InSAR), multibaseline polarimetric InSAR (PolInSAR), and SAR tomography (TomoSAR) are the advanced techniques for forest height inversion. However, accurate large-scale inversion using these techniques still faces two key problems: 1) for InSAR and PolInSAR, the existing inversion models fail to accurately describe the vertical structure, reducing the inversion performance; 2) for TomoSAR, its baseline configuration requirement is too high to reconstruct the vertical structure of the large-scale forested area. To solve these two problems, this paper proposes a high inversion accuracy forest vertical structure heterogeneity (FVSH) coherent scattering model and a small-scale tomography-aided multibaseline (Pol)InSAR (STMI) forest height inversion framework, which provide a synergic observation and inversion scheme of these multibaseline SAR techniques using machine learning approaches. The different-frequency InSAR and PolInSAR data acquired above the different types of forested areas are selected for validation. The experimental results show the effectiveness of the proposed framework.

The amplitude and phase of synthetic aperture radar (SAR) backscatter coefficients are sensitive to surface dielectric constant, canopy structure, and surface roughness. Particularly, quad-pol SAR observations which provide HH, VV, and HV/VV polarimetry information are more beneficial for crop monitoring compared with single and dual-pol data. Considering the difference of penetrability of horizontal and vertical signals over oriented canopy structure, we explores the potential of co-pol and cross-pol phase difference, like ∆ϕHH−VVand ∆ϕHH−HVand ∆ϕVV−VH, for crop phenology and structure monitoring. Besides, time-series variation of phase difference is supposed to be able to track crop growth. This paper introduces the form of the co-pol and cross-pol phase difference derived from Sinclare scattering matrix (S2×2) and the covariance matrix (C3×3). Subsequently, the mechanism of phase difference relating with the scattering phase center, the oriented crop structure, and the penetration depths of different polarization are provided. Time-series L-band quad-pol UAVSAR data which are collected from June 22 to July 17, 2012 in Winnipeg, Canada are used for the analysis of temporal and spacial phase difference characteristics over canola, soybean, and wheat fields. Results indicate significant potential of co-pol and cross-pol phase differences in crop structure and phenology monitoring. Especially, the evolution of time-series ∆ϕHH−VVis more consistent with taht of LAI measurements compared with the cross-pol phase difference.

Synthetic Aperture Radar (SAR) exhibits the capacity for comprehensive and continuous Earth observation, regardless of weather conditions and diurnal variations. Contemporary methodologies for rice field identification utilizing SAR rely upon the entirety of the rice growth cycle data, posing challenges in discerning rice cultivation within the ongoing cultivation cycle. In addressing this challenge, the present study introduces a novel metric, namely the 3-Sigmoid index (SSSI), designed to quantify the early season probability of land parcels planted rice. SSSI fully uses the crucial feature of intensity variation from low to high backscatter based on time series SAR data as paddy fields progress in their growth stages, so that we can identify in-season rice early. The method was validated in two experimental areas, and the experimental results indicate that the approach exhibits heigh accuracy in early-stage identification. Moreover, the method demonstrated successful recognition of rice fields during the tillering phase and even prior to it. In addition, the SSSI is independence from requisite prior knowledge, reference samples, and a plethora of pre-established parameters. This characteristic underscores its potential for widespread and extensive practical applications, particularly in regions characterized by persistent cloud cover, where optical remote sensing data is frequently inaccessible.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

Polarimetric calibration (PolCal) of polarimetric synthetic aperture radar (PolSAR) images is essential for quantitative remote sensing applications. The distributed targets like forest, provide polarimetric constrains for PolCal, for example, reflection symmetry constrain. In this paper, we find that the helix scattering has a negative relationship with reflection symmetric distributed targets and is useful in PolCal. We first examined the tolerance of helix-ratio feature to polarimetric distortions, and then developed an algorithm to calibrate PolSAR images with this feature. Experiments with Chinese X-band airborne images show that PolCal with helix-ratio can improve the utilizing of distributed targets, and can improve the crosstalk accuracy.

Monitoring rice growth from the space has aroused the interests of researchers from all over the world. Previous researches about rice phenology focused on dividing paddy rice into different phenological intervals, without knowing the specific date when a certain phenological stage starts. In this paper, different characteristics of the start of three important phenological stages are analyzed. A set of C-band SAR images acquired by SENTINEL-1 has been used to retrieve the start of three key phenological stages (leaf development, stem elongation and inflorescence emergence) of paddy rice. A dynamic time warping method was applied for the alignment of SAR time series curves, which are constructed using backscattering coefficients of different polarimetric channels (VH, VV and VH/VV ratio). Results show that all three kinds of time series curves show great potential in detecting start of leaf development of rice and VH/VV curves perform better in recognizing start of inflorescence emergence.

This paper explored the possibility of using the Sentinel-1 GRD Synthetic Aperture Radar (SAR) data to detect the flowering period of canola. A parameter called pseudo scattering entropy (Hc) was used in this research for canola flowering period detection. The relationship between this parameter and the flowering period of canola was analyzed in this paper. Dynamic Time Warping (DTW) was used to extract the relative flowering periods of canola in two regions. Sentinel-2 data was used for auxiliary validation to compensate for the lack of field data. The results indicate that the Hc has good correlation with canola phenology and is stable in different regions, and DTW has great application value in phenology alignment. Meanwhile, SAR data with higher temporal resolution can improve accuracy.

In this paper, Synthetic Aperture Radar (SAR) images including high resolution Gaofen-3 images and medium resolution Sentinel-1 images are used to detect the new buildings, and the detection results of different resolutions were compared and analyzed. The SAR image change detection algorithm based on likelihood ratio change matrix clustering was used, and the new buildings were extracted by threshold from the detected changes. The detection results show that images acquired by the two sensors have good performance on new building detection. However, the noise and geometric distortion in urban area of Gaofen-3 image reduce the detection accuracy, while the resolution of Sentinel-1 is insufficient for the detection of new building with small size.

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

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than -20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

For forest with complex structure, the vertical structure backscatter is influenced by a combination of factors, including the frequencies of the radar waves and the forest biophysical parameters (i.e., density, species). The backscatter-induced error is thus a critical element in limiting the accuracy of polarimetric synthetic aperture radar interferometry (Pol-InSAR) forest height inversion based on a single model. It is, therefore, necessary to select the optimal backscatter profile from the multiple possible solutions in each pixel of the test site. In this letter, the impacts of backscatter in forest height estimation based on the models of random volume over ground (RVoG) (σ > 0), RVoG (σ <; 0), and Gaussian vertical backscatter (GVB) were investigated in the complex plane, and then with the combined use of Pol-InSAR and light detection and ranging (LiDAR), a random forest (RF) classifier is trained to obtain the optimal backscatter function and Pol-InSAR forest height from the results based on the different models in each pixel. The proposed method was tested with single- and multi baseline Pol-InSAR data in the P-band, and the root-mean-square errors (RMSEs) of the proposed approach were 2.85 and 2.69 m, respectively, which represented average improvements of 20.6% and 17.7% over the optimal single-model inversion.

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China's newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.

A modified model-based polarimetric decomposition method, considering both the surface and dihedral scattering depolarization, is proposed for soil moisture retrieval. In the parameter solution, it works combining at least two polarimetric SAR images acquired simultaneously in different incidence angles. Moreover, it needs not to decide whether dihedral or surface scatter is the dominant contribution. The experiments to demonstrate the potential of the proposed approach is carried out using L-band polarimetric UAVSAR multi-incidence data in Winnipeg, Canada. The scattering mechanism of forest, grass land, urban, and barren area are analyzed compared with Yamaguchi three-component decomposition results. The performance of the soil moisture estimation algorithm is also assessed by comparing the retrieval results with in situ measurements.

This letter presents two main contributions to the data quality assessment of China's new GaoFen-3 radar satellite. First, we observed a half-pixel misregistration between the horizontal (H) and vertical (V) transmitting channels in the azimuth direction. This was determined by investigating the corner reflector (CR) response of some stripmap products of GaoFen-3 quad-pol stripmap (QPSI) mode. Second, to check whether the azimuth misregistration exists in different beams of QPSI mode, we improved the RADARSAT-2 channel checking method as a tool and evaluated more than 300 stripmap scenes. This letter confirms that the half-pixel misregistration problem, which can cause about 10% decoherence in the co-pol and cross-pol channel correlation coefficients, is common in the GaoFen-3 stripmap products of QPSI mode. Furthermore, the improved method can be considered as an effective way to fix the misregistration problem.

This paper presents a global weighted least-squares (GWLS) optimization framework for polarimetric synthetic aperture radar (PolSAR) despeckling. GWLS is simpler than other optimization methods because it does not lead to complex optimization and iterative convergence problems. Solving the PolSAR despeckling problem based on GWLS is equivalent to solving nine sparse linear systems. First, the guidance image is constructed by the span image of the PolSAR data to calculate the five-point spatially inhomogeneous Laplacian matrix. Next, the weighted sum of the Laplacian matrix and an identity matrix is used to construct a coefficient matrix for the nine linear systems. Finally, each speckle-free channel of PolSAR data is reconstructed equally and globally by solving the linear systems with the same coefficient matrix. Filtering each element of the coherency matrix equally preserves the scattering property inherent in PolSAR data. The performance of the GWLS-based method is demonstrated by both simulated and real PolSAR data. Refined Lee filter, intensity-driven adaptive-neighborhood, improved sigma filter, and nonlocal pretest filter are used in qualitative and quantitative comparison. The experiments show that the proposed method can reach a good tradeoff between noise suppressing and detail preservation and has relatively high processing efficiency.

This paper presents a spherically invariant random vector (SIRV)-based dual-domain filter for polarimetric synthetic aperture radar (PolSAR) speckle suppression. The SIRV model is a simple clutter decomposition model designed for heterogeneous scenes that decomposes the clutter into two independent domains: the texture domain and the polarimetric domain (also known as the speckle domain). The dual-domain filter takes full advantage of the SIRV model to decompose the task of PolSAR speckle suppression into polarimetric domain filtering and texture domain filtering. The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration. For this purpose, the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples. The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle. For this purpose, patch ordering-based SAR image despeckling via transform-domain filtering (SAR-POTDF), which is based on simultaneous sparse coding (SSC), is applied for the texture filtering. After the dual-domain filtering, a speckle-free PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix. The effectiveness and robustness of the proposed method was demonstrated by experiments undertaken with CETC-38 X-band, DLR F-SAR S-band, and IECAS C-band high-resolution PolSAR images. The DLR E-SAR L-band medium-resolution data were also used for comparison. The results showed that the dual-domain filtering is effective for the speckle suppression of high-resolution PolSAR images with heterogeneous and detailed scenes.

The periodic surface are often seen in agriculture, such as the planting fields of potato, sugarcane, and green onion. There are strong coherent scattering for the fields whose aligning direction is perpendicular to radar's light of sight (LOS). Enhanced backscattering coefficients induced by coherent scattering on synthetic aperture radar (SAR) images hinder the retrieval of soil moisture. The paper investigated the capability of different retrieval models in the inversion of soil moisture for bare periodic fields. The results show that the copolarized ratio and HV backscattering coefficient, which are less affected by the periodic structure, can be used to reduce the effect of coherent scattering on soil moisture inversion.

Recently, with the rapid development of earth observation (EO) techniques, the similar object information has been acquired in different regions, by the use of various sensors. It brings up a new challenge, that is, how to identify cross-domain objects. To cope with this difficulty, we first revisit the linear discriminative Laplacian eigenmaps (LDLE) in this paper, and further add a Bregman divergence (BD) based regularization term into it. The experiment results demonstrate that, the combination of LDLE and BD can learn a good linear transformation of polarimetric synthetic aperture radar (PolSAR) data and improve classification performances.

A lodged wheat detection algorithm by applying a false alarm rate to the circular-pol correlation coefficient (CCC) and the total scattered power (Span) is proposed. The CCC is first used to identify non-lodged wheat, which is reflection symmetry. The Span feature is introduced to distinguish lodged wheat from canola in the study area, according to their large difference in scattering intensity. The Polarimertric synthetic aperture radar (PolSAR) image acquired by the Radarsat-2 satellite over the Yigen farmland of China, was used to validate the effectiveness of the proposed approach. The results indicate the potential of using only post-event PolSAR image to detect the lodged wheat.

Land-cover classification in polarimetric synthetic aperture radar images is a vital technique that has been developed for years. The Wishart distribution, which the polarimetric coherence matrix obeys, has been researched to design the well-known Wishart classifier. This model is appropriate for homogeneous scenes, but it usually fails in reality when a category consists of several subcategories or clusters. Therefore, a simple but powerful sample-merging strategy is proposed to generate representative subcenters, based on a dissimilarity measure. In addition, a weighted likelihood-ratio criterion is also proposed to further improve the performance of the Wishart distribution-based classification, based on the Wishart test statistic. Two experiments on EMISAR and UAVSAR data sets confirm that combining the proposed strategies can achieve better results than can the Wishart classifier and the other existing methods.

In this paper, we investigate the separability of targets with weak backscattering on synthetic aperture radar (SAR) images by using of an unsupervised classification method. This technique is a combination of the Cloude target decomposition and the likelihood ratio test based on complex Wishart distribution for the polarimetric covariance matrix. The polarimetric SAR (PolSAR) image is initially classified by the H - α plane into eight classes. The dissimilar distance measure is derived from the statistical test of equality of covariance matrices. Significant improvement of the classification results are observed for weak backscattering targets in iterations. The effectiveness of this algorithm is demonstrated using a Radarsat-2 PolSAR image in C band and an ALOS PALSAR PolSAR image in L band.

This study investigates the characteristics of periodic surface in agriculture by the use of C-band polarimetric synthetic aperture radar (PolSAR) imagery. The scattering characteristics of the periodic potato fields in different directions are highlighted using a set of polarimetric parameters. Enhanced coherent scattering is observed when the alignment direction of ridging patterns is perpendicular to radar's line of sight (LOS). There are higher copolarized backscattering coefficients and unaffected cross-polarized backscattering coefficient for the coherent scattering. The increased copolarized correlation coefficient, reduced entropy and polarimetric alpha angle indicate that the induced coherent scattering has small scattering randomness and the odd scattering is its dominant scattering mechanism.

It is well known that polarimetric synthetic aperture radar (PolSAR) backscattering features are highly influenced by the variation of incidence angle (VIA), which usually hampers the classification of most grazing-angle-sensitive targets, such as land and ocean targets. To relieve this issue, various feature extraction approaches have been suggested to enhance the class discriminability while reducing the observed feature dimensionality. The Laplacian eigenmap-based dimension reduction (DR) has been proven to be an effective way to deal with VIA problems, provided that the manifold parameters [e.g., the heat kernel (HK)] have been optimally sought, which is often difficult in practice. In this letter, an adaptive Laplacian eigenmap-based DR method is presented to find a learned subspace where the local geometry with discriminative prior knowledge is preserved as much as possible while near optimal HK and scale factor parameters are automatically identified. The learned feature representation is then employed for the subsequent classification. The improved Laplacian eigenmap algorithm was validated by three uninhabited-aerial-vehicle-synthetic-aperture-radar L-band PolSAR images from the Gulf Deepwater Horizon oil spill, which were clearly impacted by the VIA phenomenon. The experimental results showed that the proposed algorithm works well in ocean target discrimination compared with the current common methods.

The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of [0, 255]. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.

The sensitivity of polarimetric parameters to different residue patterns during harvest has been investigated in the paper. Several crop residue cover types called residue patterns during harvest have been selected. They include harvested wheat fields with burned and non-burned straws, the swathed oilseed rapes with different moisture, the rape swathes aligned in different directions, the non-lodging wheat and lodging wheat before harvest. A set of polarimetric parameters include backscattering coefficients, coherence coefficients, polarimetric phase differences, and variables derived from Cloude decomposition are made use of. Sensitivity analysis was conducted on one Radarsat-2 quad-pol image and one TerraSAR-X dual-pol image acquired over a farmland of China. The results indicate that polarimetric parameters have various responses to different residue patterns. The residue amounts, direction and crop harvest state are the main factors that result in the fluctuation of polarimetric scattering characteristics. In addition, it is revealed that multi-polarimetric image with short wavelength has the potential to guide the farming behaviors during harvest.