scholarly journals A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2459 ◽  
Author(s):  
Xing Peng ◽  
Xinwu Li ◽  
Changcheng Wang ◽  
Haiqiang Fu ◽  
Yanan Du
Keyword(s):  
Maximum Likelihood ◽  
Synthetic Aperture Radar ◽  
Structural Parameters ◽  
Synthetic Aperture ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Adaptive Approach ◽  
Forest Height ◽  
Iterative Adaptive Approach ◽  
Aperture Radar

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.

scholarly journals Parameter estimation in multi-antenna system based on iterative adaptive approach

2018 ◽  
Vol 232 ◽  
pp. 04064
Author(s):  
Lihua Lei ◽  
Ju Zhou
Keyword(s):  
Parameter Estimation ◽  
Synthetic Aperture Radar ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Motion Parameter ◽  
Synthetic Aperture ◽  
Target Velocity ◽  
Adaptive Approach ◽  
Iterative Adaptive Approach ◽  
Aperture Radar

As one of the main means for remote sensing and detecting, synthetic aperture radar is playing more important role in many fields such as country reconnaissance, ocean observation, environment disaster monitoring and military reconnaissance. The synthetic aperture radar system based on multi-antenna technology can achieve high resolution of still image and estimate the motion parameter. The echo model of target is set up for motion parameter estimation and the performance of system parameter estimation is given according to the moving target velocity estimation method based on iterative adaptive approach.

scholarly journals Forest structure dependency analysis of L-band SAR backscatter

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10055
Author(s):  
Yongjie Ji ◽  
Jimao Huang ◽  
Yilin Ju ◽  
Shipeng Guo ◽  
Cairong Yue
Keyword(s):  
Synthetic Aperture Radar ◽  
Linear Correlation ◽  
Forest Structure ◽  
Forest Biomass ◽  
Synthetic Aperture ◽  
Forest Species ◽  
Canopy Density ◽  
Forest Height ◽  
L Band ◽  
Aperture Radar

Forest structure plays an important role in forest biomass inversion using synthetic aperture radar (SAR) backscatter. Synthetic aperture radar (SAR) sensors with long-wavelength have the potentiality to provide reliable and timely forest biomass inversion for their ability of deep penetration into the forest. L-band SAR backscatter shows useful for forest above-ground biomass (AGB) estimation. However, the way that forest structure mediating the biomass-backscatter affects the improvement of the related biomass estimation accuracy. In this paper, we have investigated L-band SAR backscatter sensitivity to forests with different mean canopy density, mean tree height and mean DBH (diameter at breast height) at the sub-compartment level. The forest species effects on their relationship were also considered in this study. The linear correlation coefficient R, non-linear correlation parameter, Maximal Information Coefficient (MIC), and the determination coefficient R2 from linear function, Logarithmic function and Quadratic function were used in this study to analyze forest structural properties effects on L-band SAR backscatter. The HV channel, which is more sensitive than HH to forest structure parameters, was chosen as the representative of SAR backscatter. 6037 sub-compartment were involved in the analysis. Canopy density showed a great influence on L-band backscatter than mean forest height and DBH. All of the R between canopy density and L-band backscatter were greater than 0.7 during the forest growth cycle. The sensitivity of L-band backscatter to mean forest height depends on forest canopy density. When canopy density was lower than 0.4, R values between mean forest height are smaller than 0.5. In contrast, the values of R were greater than 0.8 if canopy density was higher than 0.4. The sensitivity SAR backscatter to DBH fluctuated with canopy density, but it only showed obvious sensitivity when canopy density equals to 0.6, where both the linear and non-liner correlation values are higher than others. However, their effects on L-bang HV backscatter are affected by forest species, the effects on three forest structural parameters depend on tree species.

scholarly journals Two-Dimensional Ship Velocity Estimation Based on KOMPSAT-5 Synthetic Aperture Radar Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1474 ◽  
Author(s):  
Minyoung Back ◽  
Donghan Kim ◽  
Sang-Wan Kim ◽  
Joong-Sun Won
Keyword(s):  
Synthetic Aperture Radar ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Synthetic Aperture ◽  
Minimum Size ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Synthetic Aperture Radar Data ◽  
Mean Square Errors ◽  
Aperture Radar

Continuously accumulating information on vessels and their activities in coastal areas of interest is important for maintaining sustainable fisheries resources and coastal ecosystems. The speed, heading, sizes, and activities of vessels in certain seasons and at certain times of day are useful information for sustainable coastal management. This paper presents a two-dimensional vessel velocity estimation method using the KOMPSAT-5 (K5) X-band synthetic aperture radar (SAR) system and Doppler parameter estimation. The estimation accuracy was evaluated by two field campaigns in 2017 and 2018. The minimum size of the vessel and signal-to-clutter ratio (SCR) for optimum estimation were determined to be 20 m and 7.7 dB, respectively. The squared correlation coefficient R2 for vessel speed and heading angle were 0.89 and 0.97, respectively, and the root-mean-square errors of the speed and heading were 1.09 m/s (2.1 knots) and 17.9°, respectively, based on 19 vessels that satisfied the criteria of minimum size of vessel and SCR. Because the K5 SAR is capable of observing a selected coastal region every day by utilizing various modes, it is feasible to accumulate a large quantity of vessel data for coastal sea for eventual use in building a coastal traffic model.

scholarly journals A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4839
Author(s):  
Kong ◽  
Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Estimation Accuracy ◽  
Sample Covariance ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aperture Radar

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

Application of a maximum likelihood algorithm to the detection of targets in synthetic aperture radar (SAR) images

1994 ◽  
Author(s):  
William H. Schoendorf ◽  
Thomas L. Marzetta ◽  
Leonid I. Perlovsky ◽  
Elizabeth A. Martinsen ◽  
Ronald G. Wallace ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Maximum Likelihood Algorithm ◽  
Sar Images ◽  
Aperture Radar

scholarly journals Snow Water Equivalent Change Mapping from Slope Correlated InSAR Phase Variations

2021 ◽  
Author(s):  
Jayson Eppler ◽  
Bernhard T. Rabus ◽  
Peter Morse
Keyword(s):  
Synthetic Aperture Radar ◽  
Snow Water Equivalent ◽  
Phase Unwrapping ◽  
Passive Microwave ◽  
Synthetic Aperture ◽  
Estimation Methods ◽  
Glacier Mass Balance ◽  
Drainage Basins ◽  
Snow Water ◽  
Aperture Radar

Abstract. Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive-microwave, which is limited to coarse spatial resolutions of 25 km or greater, and to areas without significant topographic variation. Passive-microwave also has a negative bias for large SWE. Repeat-pass synthetic aperture radar interferometry (InSAR) as an alternate technique allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because: (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence and; (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of < 1 km, without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 spotlight-mode dataset near Inuvik, NWT, Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas and in areas with high SWE (> 150 mm), thus providing complementary coverage to other passive- and active-microwave based SWE estimation methods. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems. In application, a first order analysis of SWE trends within three drainage basins suggests that differences between basin-level accumulations are a function of major landcover types, and that re-vegetation following a forest-tundra fire that occurred over 50 years ago continues to affect the spatial distribution of SWE accumulation in the study area.

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