scholarly journals Parameter Estimation of Attribute Scattering Center Based on Water Wave Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhangkai Zhou ◽  
Yihan Li
Keyword(s):  
Parameter Estimation ◽  
Water Wave ◽  
Single Scattering ◽  
High Energy ◽  
Sar Image ◽  
Original Image ◽  
Image Domain ◽  
Scattering Center ◽  
Parameterized Model ◽  
Center Parameter

For the problem of attribute scattering center parameter estimation in synthetic aperture radar (SAR) image, a method based on the water wave optimization (WWO) algorithm is proposed. First, the segmentation and decoupling of high-energy regions in SAR image are performed in the image domain to obtain the representation of a single scattering center. Afterwards, based on the parameterized model of the attribute scattering center, an optimization problem is constructed to search for the optimal parameters of the separated single scattering center. In this phase, the WWO algorithm is introduced to optimize the parameters. The algorithm has powerfully global and local searching capabilities and avoids falling into local optimum while ensuring the optimization accuracy. Therefore, the WWO algorithm could ensure the reliability of scattering center parameter estimation. The single scattering center after solution is eliminated from the original image and the residual image is segmented into high-energy regions, so the parameters of the next scattering center are estimated sequentially. Finally, the parameter set of all scattering centers in the input SAR image can be obtained. In the experiments, firstly, the parameter estimation verification is performed based on the SAR images in the MSTAR dataset. The comparison of the parameter estimation results with the original image and the reconstruction based on the estimated parameter set reflect the effectiveness of the proposed method. In addition, the experiment is also conducted using the SAR target recognition algorithms based on the estimated attribute parameters. By comparing the recognition performance with other parameter estimation algorithms under the same conditions, the performance superiority of the proposed method in attribute scattering center parameter estimation is further demonstrated.

Monte Carlo Modelling of Secondary Electron Yield from Rough Surfaces

1990 ◽  
Vol 48 (1) ◽  
pp. 422-423
Author(s):  
John C. Russ
Keyword(s):  
Monte Carlo ◽  
Energy Loss ◽  
Secondary Electron ◽  
Secondary Electron Emission ◽  
Analytical Calculation ◽  
Mean Free Path ◽  
Single Scattering ◽  
High Energy ◽  
Secondary Electron Yield ◽  
Electron Yield

Monte-Carlo programs are well recognized for their ability to model electron beam interactions with samples, and to incorporate boundary conditions such as compositional or surface variations which are difficult to handle analytically. This success has been especially powerful for modelling X-ray emission and the backscattering of high energy electrons. Secondary electron emission has proven to be somewhat more difficult, since the diffusion of the generated secondaries to the surface is strongly geometry dependent, and requires analytical calculations as well as material parameters. Modelling of secondary electron yield within a Monte-Carlo framework has been done using multiple scattering programs, but is not readily adapted to the moderately complex geometries associated with samples such as microelectronic devices, etc.This paper reports results using a different approach in which simplifying assumptions are made to permit direct and easy estimation of the secondary electron signal from samples of arbitrary complexity. The single-scattering program which performs the basic Monte-Carlo simulation (and is also used for backscattered electron and EBIC simulation) allows multiple regions to be defined within the sample, each with boundaries formed by a polygon of any number of sides. Each region may be given any elemental composition in atomic percent. In addition to the regions comprising the primary structure of the sample, a series of thin regions are defined along the surface(s) in which the total energy loss of the primary electrons is summed. This energy loss is assumed to be proportional to the generated secondary electron signal which would be emitted from the sample. The only adjustable variable is the thickness of the region, which plays the same role as the mean free path of the secondary electrons in an analytical calculation. This is treated as an empirical factor, similar in many respects to the λ and ε parameters in the Joy model.

Full-Polarization Scattering Center Extraction and Parameter Estimation: P-ESPRIT Algorithm

2011 ◽  
Vol 30 (8) ◽  
pp. 1963-1967
Author(s):  
Da-hai Dai ◽  
Xue-song Wang ◽  
Shi-qi Xing ◽  
Shun-ping Xiao
Keyword(s):  
Parameter Estimation ◽  
Scattering Center ◽  
Esprit Algorithm

scholarly journals Rotating Parabolic-Reflector Antenna Target in SAR Data: Model, Characteristics, and Parameter Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Bin Deng ◽  
Hong-Qiang Wang ◽  
Yu-Liang Qin ◽  
Sha Zhu ◽  
Xiang Li
Keyword(s):  
Parameter Estimation ◽  
Data Model ◽  
Target Recognition ◽  
Image Interpretation ◽  
Likelihood Estimator ◽  
Parabolic Reflector ◽  
Time Frequency ◽  
Aspect Angle ◽  
Multiple Parameters ◽  
Scattering Center

Parabolic-reflector antennas (PRAs), usually possessing rotation, are a particular type of targets of potential interest to the synthetic aperture radar (SAR) community. This paper is aimed to investigate PRA’s scattering characteristics and then to extract PRA’s parameters from SAR returns, for supporting image interpretation and target recognition. We at first obtain both closed-form and numeric solutions to PRA’s backscattering by geometrical optics (GO), physical optics, and graphical electromagnetic computation, respectively. Based on the GO solution, a migratory scattering center model is at first presented for representing the movement of the specular point with aspect angle, and then a hybrid model, named the migratory/micromotion scattering center (MMSC) model, is proposed for characterizing a rotating PRA in the SAR geometry, which incorporates PRA’s rotation into its migratory scattering center model. Additionally, we in detail analyze PRA’s radar characteristics on radar cross-section, high-resolution range profiles, time-frequency distribution, and 2D images, which also confirm the models proposed. A maximal likelihood estimator is developed for jointly solving the MMSC model for PRA’s multiple parameters by optimization. By exploiting the aforementioned characteristics, the coarse parameter estimation guarantees convergency upon global minima. The signatures recovered can be favorably utilized for SAR image interpretation and target recognition.

scholarly journals An Energy-Based SAR Image Segmentation Method with Weighted Feature

2019 ◽  
Vol 11 (10) ◽  
pp. 1169 ◽  
Author(s):  
Yu Wang ◽  
Guoqing Zhou ◽  
Haotian You
Keyword(s):  
Image Segmentation ◽  
Spectral Feature ◽  
Curvelet Transform ◽  
Structural Features ◽  
Image Texture ◽  
Sar Image ◽  
Segmentation Method ◽  
Multiple Features ◽  
Image Domain ◽  
Sar Image Segmentation

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels’ feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model. In the RJMCMC algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.

Strain measurement in metallic glasses and metallic-glass-matrix composites by means of x-ray scattering

2005 ◽  
Vol 903 ◽  
Author(s):  
Todd Hufnagel ◽  
Ryan T. Ott ◽  
Jon Almer
Keyword(s):  
Metallic Glass ◽  
Elastic Strain ◽  
Glass Matrix ◽  
Amorphous Materials ◽  
Single Scattering ◽  
High Energy ◽  
Matrix Composites ◽  
Crystalline Phases ◽  
Metallic Glass Matrix Composite ◽  
Glass Matrix Composites

AbstractThe availability of high-energy synchrotron beamlines has made it possible to study the development of elastic strain in crystalline phases in metallic-glass-matrix compositesin situduring loading, and to measure elastic strain in amorphous materials as well. In this paper, we discuss experimental techniques and data analysis for these measurements and give examples. We show that the strain in both the amorphous and crystalline phases in a metallic-glass-matrix composite can be extracted from a single scattering pattern.

scholarly journals HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-polarized SAR Image

2020 ◽  
Author(s):  
Zhongling Huang
Keyword(s):  
Frequency Analysis ◽  
Clustering Algorithm ◽  
Sar Image ◽  
Image Domain ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Scattering Mechanisms ◽  
Deep Embedding ◽  
Sar Data ◽  
Complex Valued

<div> <div> <div> <p>Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the ’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded fea- tures and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single- polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen- 3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex- valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications. </p> </div> </div> </div>

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