Comparison between Microwave Coherent and Incoherent Scattering Models in Corn Field

[Objective] The aim was to compare and analyze microwave coherent and incoherent scattering models in a corn field. [Method] In the research, based on a coherent scattering model （Stile）, we proposed a coherent scattering model exclusive for corn, in which, physical optics （PO） and infinite-length dielectric cylinder were used to calculate single-scattering matrices of corn leaves and stalks. In addition, coherent components produced from interaction among the scattering mechanisms were also considered and this coherent model was compared with the Michigan Mi- crowave Canopy Scattering （MIMICS） model. The measured data in a corn filed in Gongzhuling in Jilin Province were used as the input parameters of the coherent and incoherent models. We simulated backscattering coefficients of VV and HH po- larization at L and C bands and made a comparison between the simulation results. [Result] The simulation results at L-band were poor, which indicated that we could not find regularity at early growth stage of vegetation. In addition, comparisons be- tween coherent and incoherent scattering models proved that the coherence triggered by the scattering mechanism was small. [Conclusion] In the research, we analyzed differences between coherent and incoherent scattering models with change of incident angle, and further analysis on the differences with change of vegetation and soil needed to be made in future.

Generative adversarial network-based atmospheric scattering model for image dehazing

This paper presents a trainable Generative Adversarial Network(GAN)-based end-to-end system for image dehazing,which is named the DehazeGAN.DehazeGAN can be used for edge computing-based applications,such as roadside monitoring.It adopts two networks:one is generator(G),and the other is discriminator(D).The G adopts the U-Net architecture,whose layers are particularly designed to incorporate the atmospheric scattering model of image dehazing.By using a reformulated atmospheric scattering model,the weights of the generator network are initialized by the coarse transmission map,and the biases are adaptively adjusted by using the previous round's trained weights.Since the details may be blurry after the fog is removed,the contrast loss is added to enhance the visibility actively.Aside from the typical GAN adversarial loss,the pixel-wise Mean Square Error(MSE)loss,the contrast loss and the dark channel loss are introduced into the generator loss function.Extensive experiments on benchmark images,the results of which are compared with those of several state-of-the-art methods,demonstrate that the proposed DehazeGAN performs better and is more effective.

Calculating the shading reduction coefficient of photovoltaic system efficiency using the anisotropic sky scattering model

The front-row shading reduction coefficient is a key parameter used to calculate the system efficiency of a photovoltaic(PV)power station.Based on the Hay anisotropic sky scattering model,the variation rule of solar radiation intensity on the surface of the PV array during the shaded period is simulated,combined with the voltage-current characteristics of the PV modules,and the shadow occlusion operating mode of the PV array is modeled.A method for calculating the loss coefficient of front shadow occlusion based on the division of the PV cell string unit and Hay anisotropic sky scattering model is proposed.This algorithm can accurately evaluate the degree of influence of the PV array layout,wiring mode,array spacing,PV module specifications,and solar radiation on PV power station system efficiency.It provides a basis for optimizing the PV array layout,reducing system loss,and improving PV system efficiency.

A 3D Geometry-Based Scattering Model for Vehicleto-Vehicle Wideband MIMO Relay-Based Cooperative Channels

In this paper, a three-dimensional(3D) geometry- based stochastic scattering model(GBSSM) for wideband multi-input multi-output(MIMO) vehicle-to-vehicle(V2V) relay-based cooperative fading channel based on geometrical three-cylinder is proposed. Non-line-of-sight(NLOS) propagation condition is assumed in amplify-and-forward(AF) cooperative networks from the source mobile station(S) to the destination mobile station(D) via the mobile relay station(R). We extend the proposed narrowband model to wideband and also introduce the carrier frequency and bandwidth into the model. To avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions, the channel is realized first. By using the realized channel matrix, the channel properties are further investigated.

A novel synthetic aperture radar scattering model for sea surface with breaking waves

In this study a novel synthetic aperture radar(SAR)scattering model for sea surface with breaking waves is proposed.Compared with existing models,the proposed model considers an empirical relationship between wind speed and wave breaking scattering to present the contribution of wave breaking.Moreover,the scattering weight factor p,and wave breaking rate q,are performed to present the contribution of the quasi-specular scattering term,Bragg scattering term,and wave breaking scattering term to the total scattering from the sea surface.To explore the modeling accuracy of sea-surface scattering,a simulated normalized radar cross-section(NRCS)and measured NRCS are compared.The proposed model generated the simulated NRCS and a matching GF-3 dataset was used for the measured NRCS.It was revealed that the performance of the VV polarization of our model was much better than that of HH polarization,with a correlation of 0.91,bias of-0.14 dB,root mean square error(RMSE)of 1.26 dB,and scattering index(SI)of-0.11.In addition,the novel model is explored and compared with the geophysical model of CMODs and satellite-measured NRCS from GF-3 SAR wave mode imagery.For an incidence angle 40°–41°,the relationship between the NRCS and wind speed,relative wind direction is proposed.As with the SAR-measured NRCS,the performance of VV polarization was much better than HH polarization,with a correlation of 0.99,bias of-0.25 dB,RMSE of 0.64 dB,and SI of-0.04.

Bi-Spectrum Scattering Model for Conducting Randomly Rough Surface

A scattering model is developed to predict the scattering coefficient of a conducting randomly rough surface by analyzing the randomly rough surface in the spectral domain using the bi-spectrum method. For common randomly rough surfaces without obvious two-scale characteristics, a scale-compression filter can divide the auto-correlation spectrum into two parts with different correlation lengths. The Kirchhoff approximation and the small perturbation method are used to obtain the surface field, then a bistatic scattering model, the bi-spectrum model (BSM), is used to derive an explicit expression from the surface field. Examples using the integral equation model (IEM), finite difference of the time domain (FDTD) method, and BSM show that the BSM accuracy is acceptable and its range of validity is similar to IEM. BSM can also be extended to a scattering model for dielectric randomly rough surfaces.

Theoretical Models of Light Scattering Applied in Sizing Particles in Coal Water Slurry

Advantges and disadvantage of Mie scattering model and Fraunhofer diffraction model are discussed. The result shows that 1) the Fraunhofer diffraction model is simple in design and fast in operation, which is quite suitable for on-line control and 2) the intensity and energy distribution of diffracted light of both the Mie scattering model and the Fraunhofer theoretical model are compared and researched. Feasibility of using the Fraunhofer diffraction model to replace the Mie scattering model in measuring particles in coal water slurry is demonstrated.

Haze removal for UAV reconnaissance images using layered scattering model

During the unmanned aerial vehicles （UAV） reconnaissance missions in the middle-low troposphere, the reconnaissance images are blurred and degraded due to the scattering process of aerosol under fog, haze and other weather conditions, which reduce the image contrast and color fidelity. Considering the characteristics of UAV itself, this paper proposes a new algorithm for dehazing UAV reconnaissance images based on layered scattering model. The algorithm starts with the atmosphere scattering model, using the imaging distance, squint angle and other metadata acquired by the UAV. Based on the original model, a layered scattering model for dehazing is proposed. Considering the relationship between wave-length and extinction coefficient, the airlight intensity and extinction coefficient are calculated in the model. Finally, the restored images are obtained. In addition, a classification method based on Bayesian classification is used for classifica- tion of haze concentration of the image, avoiding the trouble of manual working. Then we evaluate the haze removal results according to both the subjective and objective criteria. The experimental results show that compared with the origin image, the comprehensive index of the image restored by our method increases by 282.84%, which proves that our method can obtain excellent dehazing effect.

Bi-Spectrum Scattering Model for Dielectric Randomly Rough Surface

The bistatic scattering model is offen used for remote microwave sensing. The bi-spectrum model (BSM) for conducting surfaces was used to develop a scattering model for dielectric randomly rough surfaces to estimate their bistatic scattering coefficients. The model for dielectric rough surfaces differs from the BSM for a conducting surface by including Fresnell reflection and transmission from dielectric rough surfaces. The bistatic scattering coefficients were defined to satisfy the reciprocal theorem. Values calculated using the BSM for dielectric randomly rough surfaces compare well with those of the integral equation model (IEM) and with experimental data, showing that the BSM accuracy is acceptable and its range of validity is similar to that of IEM while the BSM expression is simpler than that of IEM.

An InSAR scattering model for multi-layer snow based on QuasiCrystalline Approximation(QCA) theory

Snow-cover parameters are important indicator factors for hydrological models and climate change studies and have typical vertical stratification characteristics. Remote sensing can be used for large-scale monitoring of snow parameters. In SAR(Interferometric Synthetic Aperture Radar) technology has advantages in detecting the vertical structure of snow cover. As a basis of snow vertical structure detection using In SAR, a scattering model can reveal the physical process of interaction between electromagnetic waves and snow. In recent years, the In SAR scattering model for single-layer snow has been fully studied;however, it cannot be applied to the case of multi-layer snow. To solve this problem, a multi-layer snow scattering mode is proposed in this paper, which applies the QCA(Quad-Crystal Approximation) theory to describe the coherent scattering characteristics of snow and introduces a stratification factor to describe the influence of snow stratification on the crosscorrelation of SAR echoes. Based on the proposed model, we simulate an In SAR volumetric correlation of different types of multi-layer snow at the X band(9.6 GHz). The results show that this model is suitable for multi-layer snow, and the sequence of sub-layers of snow has a significant influence on the volumetric correlation. Compared to the single layer model, the multi-layer model can predict a polarization difference in the volumetric correlation more accurately and thus has a wider scope of application. To make the model more available for snow parameter inversion, a simplified multi-layer model was also developed.The model did not have polarization information compared to that of the full model but showed good consistency with the full model. The phase of the co-polarization In SAR volumetric correlation difference is more sensitive to snow parameters than that of the phase difference of the co-polarization In SAR volumetric correlation and more conducive to the development of a parameter-inversion algorithm. The model can be applied to deepen our understanding of In SAR scattering mechanisms and to develop a snow parameter inversion algorithm.

Physical model of acoustic forward scattering by cylindrical shell and its experimental validation

Research on the underwater target scattering can provide important theoretical support for target detection. The scattering model of cylindrical shell is established in this paper. It is found that the forward target strength is much stronger and varies with angles of incident wave less significantly than backward target strength. The received forward signal strength fluctuates with the target moving due to the interference between direct signal and scattering signal, which is most significant when the target approaches the baseline. An experiment is carried out in an anechoic tank to validate the scattering model. The method of acquisiting forward scattering in the tank is proposed. The forward and the backward target strengths are achieved by using the pulse compression technology, and they axe about 3dB less than the modeling results. The forward scattering phenomena of quiescent and moving targets are measured, which are similar to modeling results with different target types.

Determining the oil weighting factor of oil-water emulsion based on the fractional Weierstrass scattering model

The formation of oil-water emulsion often occurs when oil is spilled into the ocean. Oil weighting factor of oil-water emulsion is one of the most important parameters for emergent oil-spill microwave monitoring. A new method is proposed here to evaluate the oil weighting factor based on fractional Weierstrass scattering model. By using the proposed method, we analyze the Uninhabited Aerial Vehicle Synthetic Aperture Radar(UAVSAR) L-band fully polarimetric data acquired during 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico. The result shows that our method performs well in evaluating oil weighting factor of oil-covered area.

Adaptive polarimetric decomposition using incoherent ground scattering models without reflection symmetry assumption

Most of the current incoherent polarimetric decompositions employ coherent models to describe ground scattering;however,this cannot truly reflect the fact especially in natural ground surfaces.This paper proposes a highly adaptive decomposition with incoherent ground scattering models(ADIGSM).In ADIGSM,Neumann’s adaptive model is employed to describe volume scattering,and to explain cross-polarized power in remainder matrix,so that we can obtain orientation angle randomness for both volume scattering and the dominant ground scattering.The computation of volume scattering parameters is strictly constrained for non-negative eigenvalues,while the volume scattering parameters that explain the most cross-polarized power are selected.When applying ADIGSM to NASA’s UAVSAR data,the negative component powers were obtained in quite a few forest pixels.Compared with several newest decompositions,the volume scattering power is obviously lowered,especially in areas dominated by surface scattering or double bounce scattering.The orientation angle randomness of each component is reasonable as well.ADIGSM has potential to be applied in the fields such as PolSAR image classification,land cover mapping,speckle filtering,soil moisture and roughness estimation,etc.

Generalized 3D Scattering Channel Model with MIMO Antenna Systems

In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the azimuth and elevation planes in an environment where uniformly distributed scatterers are assumed to be present in hemispheroids around the base station(BS) and mobile station(MS). Using this channel model, we first derive the closed-form expression for the joint and marginal probability density functions of the angle-of-arrival and time-of-arrival measured at the BS and the MS corresponding to the azimuth and elevation angles. Next, we derive an expression for the Doppler spectral distribution caused by motion of the MSs. Furthermore, we analyze the performance of multiple-input multiple-output antenna systems numerically. The results show that the proposed 3D scattering channel model performs better than previously proposed two-dimensional(2D) models for indoor and outdoor environments. We compare the results with previous scattering channel models and measurement results to validate the generalizability of our model.

Hybrid micromotion-scattering center model for synthetic aperture radar micromotion target imaging

Micromotion is an important target feature, although the target micromotion has an unfavorable influence on the synthetic aperture radar （SAR） image interpretation due to defocusing. This paper introduces micromotion parameters into the scattering center model to obtain a hybrid micromotion-scattering center model, and then proposes an optimization algorithm based on the maximal likelihood estimation to solve the model for jointly obtaining target motion and scattering parameters. Initial value estimation methods using targets＇ ghost images are then presented to guarantee the global and fast convergence. Simulation results show the effectiveness of the proposed algorithm especially in high precision estimation and multiple targets processing.

Scattering center modeling for low-detectable targets

The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions.This paper presents an SC modeling approach to acquire the weak SCs of LDTs.We employ the induced currents at the LDT to search SCs,and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs.Particle swarm optimization(PSO)is applied to improve the estimation results of SCs.The accuracy of the SC model built by this approach is verified by a full-wave numerical method.The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images,such as high-resolution range profile and inverse synthetic aperture radar(ISAR)images.

Time-domain compressive dictionary of attributed scattering center model for sparse representation

Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.

Image Dehazing with Hybrid λ2-λ0 Penalty Mode

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.

A Geometry-Based Non-Stationary MIMO Channel Model for Vehicular Communications

This paper derives a non-stationary multiple-input multiple-output(MIMO) from the one-ring scattering model. The proposed channel model characterizes vehicular radio propagation channels with considerations of moving base and mobile stations, which makes the angle of arrivals(AOAs) along with the angle of departures(AODs) time-variant. We introduce the methodology of including the time-variant impacts when characterizing non-stationary radio propagation channels through the geometrical channel modelling approach. We analyze the statistical properties of the proposed channel model including the local time-variant autocorrelation function(ACF) and the space cross-correlation functions(CCFs). We show that the model developed in this paper for non-stationary scenarios includes the existing one-ring wide-sense stationary channel model as its special case.

Exploitation of Electromagnetic Models for Sea Wind Speed Estimation from C-Band Sentinel-1 Images

Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cells. For this purpose, one can find two principal approaches: via electromagnetic (EM) models and empirical (EP) models. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. It is very interesting to compare wind speed estimated by the EM models, general descriptions of radar scattering from sea surface, to the one estimated by the EP models, specific descriptions for the inverse problem. Based on the comparisons, some ideas are proposed to improve the performance of the EM models for wind speed retrieval.