Gridless GLRT for Tomographic SAR Detection Using Particle Swarm Optimization Algorithm

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 heigh...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 21; s. 1 - 5
Hlavní autoři: Haddad, Nabil, Budillon, Alessandra, Hadj-Rabah, Karima, Bouaraba, Azzedine, Harkati, Lekhmissi, Benbouzid, Mohammed Amine, Schirinzi, Gilda
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Algorithms
Computational efficiency
Detectors
Generalized likelihood ratio test (GLRT) detection
Geoscience and remote sensing
gridless GLRT
Height
height estimation
Image reconstruction
Likelihood ratio
Linear programming
Minimization
Particle swarm optimization
particle swarm optimization (PSO)
Radar detection
Resolution cell
Root-mean-square errors
SAR (radar)
Synthetic aperture radar
synthetic aperture radar (SAR) tomography (TomoSAR)
Three dimensional models
Tomography
Vectors
ISSN:1545-598X, 1558-0571
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Popis
Shrnutí: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.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3485883