scholarly journals A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaozhen Ren ◽  
Yao Qin ◽  
Lihong Qiao
Keyword(s):  
3D Imaging ◽  
Three Dimensional ◽  
Imaging Method ◽  
Computationally Efficient ◽  
Theory Analysis ◽  
Imaging Geometry ◽  
Range Resolution ◽  
Sparse Array ◽  
Resolution Imaging ◽  
Cross Track

Three-dimensional (3D) imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR) high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method.

scholarly journals Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2477 ◽  
Author(s):  
Jubo Hao ◽  
Jin Li ◽  
Yiming Pi
Keyword(s):  
3D Imaging ◽  
Linear Array ◽  
Ground Plane ◽  
Three Dimensional ◽  
Terahertz Waves ◽  
Recovery Method ◽  
Imaging Algorithm ◽  
Imaging Geometry ◽  
Imaging Results ◽  
Sparse Linear Array

Due to the non-contact detection ability of radar and the harmlessness of terahertz waves to the human body, three-dimensional (3D) imaging using terahertz synthetic aperture radar (SAR) is an efficient method of security detection in public areas. To achieve high-resolution and all aspect imaging, circular trajectory movement of radar and linear sensor array along the height direction were used in this study. However, the short wavelength of terahertz waves makes it practically impossible for the hardware to satisfy the half-wavelength spacing condition to avoid grating lobes. To solve this problem, a sparse linear array model based on the equivalent phase center principle was established. With the designed imaging geometry and corresponding echo signal model, a 3D imaging algorithm was derived. Firstly, the phase-preserving algorithm was adopted to obtain the 2D image of the ground plane for each sensor. Secondly, the sparse recovery method was applied to accomplish the scattering coefficient reconstruction along the height direction. After reconstruction of all the range-azimuth cells was accomplished, the final 3D image was obtained. Numerical simulations and experiments using terahertz radar were performed. The imaging results verify the effectiveness of the 3D imaging algorithm for the proposed model and validate the feasibility of terahertz radar applied in security detection.

scholarly journals Compressive Sensing Based Three-Dimensional Imaging Method with Electro-Optic Modulation for Nonscanning Laser Radar

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 748
Author(s):  
Yulong An ◽  
Yanmei Zhang ◽  
Haichao Guo ◽  
Jing Wang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Low Cost ◽  
Functional Model ◽  
Three Dimensional ◽  
Visible Spectrum ◽  
Depth Map ◽  
Imaging Method ◽  
Practical Applications ◽  
Electro Optic

Low-cost Laser Detection and Ranging (LiDAR) is crucial to three-dimensional (3D) imaging in applications such as remote sensing, target detection, and machine vision. In conventional nonscanning time-of-flight (TOF) LiDAR, the intensity map is obtained by a detector array and the depth map is measured in the time domain which requires costly sensors and short laser pulses. To overcome such limitations, this paper presents a nonscanning 3D laser imaging method that combines compressive sensing (CS) techniques and electro-optic modulation. In this novel scheme, electro-optic modulation is applied to map the range information into the intensity of echo pulses symmetrically and the measurements of pattern projection with symmetrical structure are received by the low bandwidth detector. The 3D imaging can be extracted from two gain modulated images that are recovered by solving underdetermined inverse problems. An integrated regularization model is proposed for the recovery problems and the minimization functional model is solved by a proposed algorithm applying the alternating direction method of multiplier (ADMM) technique. The simulation results on various subrates for 3D imaging indicate that our proposed method is feasible and achieves performance improvement over conventional methods in systems with hardware limitations. This novel method will be highly valuable for practical applications with advantages of low cost and flexible structure at wavelengths beyond visible spectrum.

scholarly journals A Microwave Three-Dimensional Imaging Method Based on Optimal Wave Spectrum Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7306
Author(s):  
Yan Zhang ◽  
Baoping Wang ◽  
Yang Fang ◽  
Zuxun Song
Keyword(s):  
3D Imaging ◽  
Imaging System ◽  
Wave Spectrum ◽  
Sampling Rate ◽  
Three Dimensional ◽  
Imaging Method ◽  
Data Sampling ◽  
Spectrum Reconstruction ◽  
Memory Space ◽  
Target Wave

Limited by the Shannon–Nyquist sampling law, the number of antenna elements and echo signal data of the traditional microwave three-dimensional (3D) imaging system are extremely high. Compressed sensing imaging methods based on sparse representation of target scene can reduce the data sampling rate, but the dictionary matrix of these methods takes a lot of memory, and the imaging has poor quality for continuously distributed targets. For the above problems, a microwave 3D imaging method based on optimal wave spectrum reconstruction and optimization with target reflectance gradient is proposed in this paper. Based on the analysis of the spatial distribution characteristics of the target echo in the frequency domain, this method constructs an orthogonal projection reconstruction model for the wavefront to realize the optimal reconstruction of the target wave spectrum. Then, the inverse Fourier transform of the optimal target wave spectrum is optimized according to the law of the target reflectance gradient distribution. The proposed method has the advantages of less memory space and less computation time. What is more, the method has a better imaging quality for the continuously distributed target. The computer simulation experiment and microwave anechoic chamber measurement experiment verify the effectiveness of the proposed method.

scholarly journals True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR

2019 ◽  
Vol 11 (13) ◽  
pp. 1541 ◽  
Author(s):  
Chen ◽  
Shi ◽  
Gong ◽  
Sun ◽  
Chen ◽  
...  
Keyword(s):  
3D Imaging ◽  
Spectral Band ◽  
Three Dimensional ◽  
Principal Component ◽  
Support Vector ◽  
Imaging Method ◽  
Spectral Correlation ◽  
Spectral Bands ◽  
True Color ◽  
Band Combinations

True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D imaging method based on a 32-channel hyperspectral LiDAR (HSL) covering a 431–751 nm spectral range. We conducted two experiments, one with nine-color card papers and the other with seven different colored objects. We used the former to investigate the effect of true-color 3D imaging and determine the optimal spectral bands for compositing true-color, and the latter to explore the classification potential based on the true-color feature using polynomial support vector machine (SVM) and Gaussian naive Bayes (NB) classifiers. Since using all bands of HSL will cause color distortions, the optimal spectral band combination for better compositing the true-color were selected by principal component analysis (PCA) and spectral correlation measure (SCM); PCA emphasizes the amount of information in band combinations, while SCM focuses on correlation between bands. The results show that the true-color 3D imaging can be realized based on HSL measurements, and three spectral bands of 466, 546, and 626 nm were determined. Comparing reflectance of the three selected bands, the overall classification accuracy of seven different colored objects was improved by 14.6% and 8.25% based on SVM and NB, respectively, classifiers after converting spectral intensities into true-color information. Overall, this study demonstrated the potential of HSL system in retrieving true-color and facilitating target recognition, and can serve as a guide in developing future three-channel or multi-channel true-color LiDAR.

scholarly journals Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3563
Author(s):  
Zekun Jiao ◽  
Chibiao Ding ◽  
Longyong Chen ◽  
Fubo Zhang
Keyword(s):  
Bayesian Inference ◽  
3D Imaging ◽  
Three Dimensional ◽  
Super Resolution ◽  
Information Criterion ◽  
Radar Data ◽  
Imaging Method ◽  
Model Order Selection ◽  
Imaging Model ◽  
Imaging Performance

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

scholarly journals Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

2021 ◽  
Vol 13 (19) ◽  
pp. 3800
Author(s):  
Lei Fan ◽  
Yang Zeng ◽  
Qi Yang ◽  
Hongqiang Wang ◽  
Bin Deng
Keyword(s):  
Image Quality ◽  
Three Dimensional ◽  
Super Resolution ◽  
Radar Imaging ◽  
Imaging Method ◽  
Spectrum Estimation ◽  
High Quality ◽  
Imaging Geometry ◽  
Imaging Time ◽  
Model Training

High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

scholarly journals Three-dimensional Multicolor Subcellular Imaging by Fast Serial Sectioning Tomography for Centimeter-scale Specimens

2021 ◽  
Author(s):  
Wentao Yu ◽  
Lei Kang ◽  
Victor T. C. Tsang ◽  
Yan Zhang ◽  
Ivy H. M. Wong ◽  
...  
Keyword(s):  
High Speed ◽  
3D Imaging ◽  
Uv Light ◽  
Three Dimensional ◽  
Biological Information ◽  
Imaging Method ◽  
Tissue Preparation ◽  
Layer By Layer ◽  
Autofluorescence Imaging ◽  
Double Labeling

Rapid multicolor three-dimensional (3D) imaging for centimeter-scale specimens with subcellular resolution remains a challenging but captivating scientific pursuit. Here, we present a fast, automated, cost-effective, and versatile multicolor 3D imaging method with ultraviolet (UV) surface excitation and vibratomy-assisted sectioning, termed translational rapid ultraviolet-excited sectioning tomography (TRUST). TRUST enables exogenous molecular-specific fluorescence and endogenous content-rich autofluorescence imaging simultaneously with the help of a UV light-emitting diode and a color camera. Commonly applied tissue preparation procedures (e.g., staining or clearing) are laborious, time-consuming, and may induce detrimental effects on processed samples. In TRUST, formalin-fixed specimens are stained with real-time double labeling layer by layer along with serial widefield optical illumination with raster scanning and mechanical sectioning to improve the staining speed and reveal rich biological information. All vital organs in mice have been imaged by TRUST to demonstrate its fast, robust, and high-content multicolor 3D imaging ability. Moreover, its potential for developmental biology has also been validated by imaging entire mouse embryos (taking ~2 days for imaging the embryo at the embryonic day of 15). TRUST offers a way for multicontrast and multicolor whole-organ 3D imaging with high resolution and high speed while relieving researchers from heavy sample preparation workload.

scholarly journals MTRC-Tolerated Multi-Target Imaging Based on 3D Hough Transform and Non-Equal Sampling Sparse Solution

2021 ◽  
Vol 13 (19) ◽  
pp. 3817
Author(s):  
Yimeng Zou ◽  
Jiahao Tian ◽  
Guanghu Jin ◽  
Yongsheng Zhang
Keyword(s):  
Hough Transform ◽  
3D Imaging ◽  
Three Dimensional ◽  
Sparse Optimization ◽  
Imaging Method ◽  
Two Dimensional ◽  
Range Cell ◽  
Distributed Array ◽  
Target Imaging ◽  
3D Hough Transform

Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.

scholarly journals Three dimensional acoustic imaging technology of buried object detection

2019 ◽  
Vol 283 ◽  
pp. 04010
Author(s):  
Weihua Cong ◽  
Lisheng Zhou
Keyword(s):  
Object Detection ◽  
Image Recognition ◽  
3D Imaging ◽  
Three Dimensional ◽  
Acoustic Imaging ◽  
Imaging Method ◽  
Imaging Technology ◽  
Buried Objects ◽  
Detection Technology ◽  
Buried Object

With the development of 21th century seabed imaging sonar technology, more and more attention is paid to buried object detection technology in the world. In this paper, a low frequency and high resolution three-dimensional acoustic imaging of buried object detection method and its application example are given. Compared with the traditional two-dimensional synthetic aperture imaging, the 3D imaging technology not only solves the problem of the aliasing of the seabed formation echo and the sea floor echo, being able to provide the target buried depth, but also the 3D imaging is more helpful to the image recognition. The 3D acoustic imaging method proposed by this paper has already become the development trend of buried object detection technology. We have noticed that, different from the three-dimensional visualization of the target in the water, the three-dimensional visualization of buried objects has a serious formation image occlusion problem. In addition, the three-dimensional imaging needs to be obtained centimeter-level resolution on three dimensions for better image recognition of small buried objects, in which azimuth resolution is the bottleneck.

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