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Gridless GLRT for Tomographic SAR Detection Using Particle Swarm Optimization Algorithm
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AI
DOI：10.1109/LGRS.2024.3485883.png)
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Abstract
En 中文
The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.
Keywords:
Detectors
Accuracy
Particle swarm optimization
Linear programming
Computational efficiency
Tomography
Minimization
Image reconstruction
Geoscience and remote sensing
Vectors
Generalized likelihood ratio test (GLRT) detection
gridless GLRT
height estimation
particle swarm optimization (PSO)
synthetic aperture radar (SAR) tomography (TomoSAR)
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K