scholarly journals Suppressing Paired Echoes Caused by Stair-Step Antenna Steering in TOPS SAR Imaging

2019 ◽  
Vol 11 (21) ◽  
pp. 2544
Author(s):  
He ◽  
Zhang ◽  
Yi ◽  
Jin ◽  
Dong
Keyword(s):  
Beam Steering ◽  
Impulse Response Function ◽  
Simulated Data ◽  
Sar Images ◽  
New Approach ◽  
Absolute Phase ◽  
Steering Law ◽  
Sar Data ◽  
Antenna Steering ◽  
Progressive Scan

The use of electronically steered antennas in the azimuth dimension typically leads to a staircase-like antenna beam steering law in the Terrain Observation by Progressive Scan (TOPS) wide-swath synthetic aperture radar (SAR) data acquisition mode, which will introduce paired echoes in the focused images. This paper proposes a new approach for removing such paired echoes from TOPS SAR images based on the generalization of the ideal optimum filtering concept, which can be implemented easily in the SAR data processing. Modeling the amplitude-modulated azimuth signal shows that the absolute phase of the introduced paired echoes cannot be determined due to the random rotation angle jump time for each target, which will prevent the precise use of optimum filtering. An extended optimum filtering approach, which is originally proposed for suppressing the azimuth ambiguities in SAR images, is reintroduced in this particular case, and a new approximated and generalized form of the deconvolving filtering in the approach is redefined to accommodate the undetermined phase for both the strongest paired distortion peaks and the other peripheral peaks in the distorted impulse response function (IRF). Simulated data from a TOPS SAR mode with staircase-like beam steering are used to verify the improvement in image quality by using the new method.

scholarly journals A New Approach to the Quality Control of Slope and Aspect Classes Derived from Digital Elevation Models

2021 ◽  
Vol 13 (11) ◽  
pp. 2069
Author(s):  
M. V. Alba-Fernández ◽  
F. J. Ariza-López ◽  
M. D. Jiménez-Gamero
Keyword(s):  
Quality Control ◽  
Hypothesis Test ◽  
Simulated Data ◽  
Slope Aspect ◽  
New Approach ◽  
Reference Set ◽  
Digital Elevation ◽  
Using Data ◽  
Elevation Model ◽  
Degree Of Similarity

The usefulness of the parameters (e.g., slope, aspect) derived from a Digital Elevation Model (DEM) is limited by its accuracy. In this paper, a thematic-like quality control (class-based) of aspect and slope classes is proposed. A product can be compared against a reference dataset, which provides the quality requirements to be achieved, by comparing the product proportions of each class with those of the reference set. If a distance between the product proportions and the reference proportions is smaller than a small enough positive tolerance, which is fixed by the user, it will be considered that the degree of similarity between the product and the reference set is acceptable, and hence that its quality meets the requirements. A formal statistical procedure, based on a hypothesis test, is developed and its performance is analyzed using simulated data. It uses the Hellinger distance between the proportions. The application to the slope and aspect is illustrated using data derived from a 2×2 m DEM (reference) and 5×5 m DEM in Allo (province of Navarra, Spain).

scholarly journals Analytic Solution for Double Optical Metasurface Beam Scanners

2021 ◽  
Author(s):  
Jingru Wang ◽  
Yuehe Ge ◽  
Zhizhang (David) Chen ◽  
Zhimeng Xu ◽  
Hai Zhang
Keyword(s):  
Small Volume ◽  
Analytic Solution ◽  
Low Cost ◽  
Beam Steering ◽  
Steering System ◽  
New Approach ◽  
Blind Zone ◽  
Inverse Solutions ◽  
Control Light ◽  
Light Beams

Abstract Optical metasurfaces are researched more and more intensively for the possible realization of lightweight and compact optical devices with novel functionalities. In this paper, a new beam-steering system based on double metasurface lenses (metalenses) is proposed and developed. The proposed system is lightweight, small volume, low cost, and easy to integrate. The exact forward and inverse solutions are derived respectively using the generalized Snell’s law of refraction. Given the orientations of the double metalenses, the pointing position can be accurately determined. If the desired pointing position is given, the required metalenses’ orientations can be obtained by applied global optimization algorithms to solve nonlinear equations related to the inverse problem. The relationships of the scan region and blind zone with the system parameters are derived. The method to eliminate the blind zone is given. Comparison with double Risley-prism systems is also conducted. This work provides a new approach to control light beams.

A New Approach to Image Reconstruction in Positron Emission Tomography Using Artificial Neural Networks

1998 ◽  
Vol 09 (01) ◽  
pp. 71-85 ◽  
Author(s):  
A. Bevilacqua ◽  
D. Bollini ◽  
R. Campanini ◽  
N. Lanconelli ◽  
M. Galli
Keyword(s):  
Positron Emission Tomography ◽  
Simulated Data ◽  
Emission Tomography ◽  
Source Distribution ◽  
New Approach ◽  
High Data ◽  
Positron Emission ◽  
Physical Effects ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels).

scholarly journals Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

2019 ◽  
Vol 11 (13) ◽  
pp. 1582 ◽  
Author(s):  
Mahdianpari ◽  
Mohammadimanesh ◽  
McNairn ◽  
Davidson ◽  
Rezaee ◽  
...  
Keyword(s):  
Variable Importance ◽  
Sar Images ◽  
Object Based ◽  
Multi Temporal ◽  
Importance Analysis ◽  
Crop Mapping ◽  
Crop Classification ◽  
Sar Data ◽  
Highly Correlated ◽  
Marginal Improvement

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

scholarly journals Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

2014 ◽  
Vol 6 (4) ◽  
pp. 2989-3019 ◽  
Author(s):  
Osmar de Carvalho Júnior ◽  
Luz Maciel ◽  
Ana de Carvalho ◽  
Renato Guimarães ◽  
Cristiano Silva ◽  
...  
Keyword(s):  
Probability Density ◽  
Sar Images ◽  
New Approach ◽  
Components Analysis

Beam Steering SAR Data Processing by a Generalized PFA

2013 ◽  
Vol 51 (8) ◽  
pp. 4366-4377 ◽  
Author(s):  
Guang-Cai Sun ◽  
Mengdao Xing ◽  
Xiang-Gen Xia ◽  
Yirong Wu ◽  
Zheng Bao
Keyword(s):  
Data Processing ◽  
Beam Steering ◽  
Sar Data

scholarly journals Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

2014 ◽  
Vol 14 (7) ◽  
pp. 1835-1841 ◽  
Author(s):  
A. Manconi ◽  
F. Casu ◽  
F. Ardizzone ◽  
M. Bonano ◽  
M. Cardinali ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Southern Italy ◽  
Surface Deformation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Main Road ◽  
Rapid Mapping ◽  
X Band ◽  
Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution

2010 ◽  
Vol 439-440 ◽  
pp. 1475-1480
Author(s):  
Li Ping Zhang ◽  
Chao Wang ◽  
Hong Zhang ◽  
Bo Zhang
Keyword(s):  
High Resolution ◽  
Nearest Neighbor ◽  
Simulated Data ◽  
Automatic Target Recognition ◽  
Morphological Processing ◽  
Affine Invariant ◽  
Accuracy Rate ◽  
Sar Images ◽  
Multi Scale ◽  
Aircraft Type

Automatic target recognition is the key stage of SAR image interpretation system and has been taking a great interest to the researchers in recent years. Aiming at the issue of aircraft type recognition in high-resolution SAR images, a novel method based on multi-scale autoconvolution (MSA) affine invariant moment is proposed. First, the texture analysis and clustering method are used to segment the SAR images and then the denoising algorithm and morphological processing are applied to segmented results. Second, 29 MSA features are extracted and form a feature vector to represent the target, then the vector components are standardized by gauss normalization. In the final, the vectors are classified by using the nearest neighbor classifier and template library constructed previously. Experimental results show that the proposed method can obtain high accuracy rate with high processing speed, in which the accuracy rate of two type aircrafts with real data arrives at 85.17% and the accuracy rate of four type aircrafts with simulated data arrives at 87.85%.

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