Development of an algorithm for automatic detection of oil slicks from synthetic aperture radar (SAR) imagery in the Gulf OF Guinea

Image Fusion ◽

Low Cost ◽

Synthetic Aperture ◽

Discrete Wavelet ◽

Multi Layer Perceptron ◽

Robust Adaptive ◽

Sar Imagery ◽

Design Regularization ◽

This paper presents a novel multi-layer perceptron (MLP) based image fusion technique, which fuses two synthetic aperture radar (SAR) images, obtained from the same spatial reflectivity map, acquired with a conventional low-cost fractional synthetic aperture radar (Fr-SAR) system, enhanced via two different methodologies. The first image is enhanced using the traditional descriptive experiment design regularization (DEDR) framework through the projection onto convex solution sets (POCS) method; the second image is enhanced with the DEDR framework by incorporating the robust adaptive spatial filtering (RASF) solution operator. This work describes a MLP based technique applied to the pixel level multi-focus fusion problem characterized by the use of image windows with the idea of reducing noise and determining which pixel is clearer between the two images. Experimental results show that the proposed novel method outperforms the discrete wavelet transform based most competing approach.

Wavelet transform and particle swarm optimization algorithms for automatic detection of internal wave from synthetic aperture radar

Nonlinear Ocean Dynamics ◽

10.1016/b978-0-12-820785-7.00005-8 ◽

2021 ◽

pp. 247-274

Author(s):

Maged Marghany

Keyword(s):

Particle Swarm Optimization ◽

Wavelet Transform ◽

Internal Wave ◽

Optimization Algorithms ◽

Particle Swarm ◽

Automatic Detection ◽

Synthetic Aperture ◽

Swarm Optimization ◽

Refocusing of Multiple Moving Targets Based on the Joint Sparse Processing of One Channel Synthetic Aperture Radar Imagery Patches

Electronics ◽

10.3390/electronics8111215 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1215 ◽

Cited By ~ 1

Author(s):

Xin Wang ◽

Ling Qiao

Keyword(s):

Moving Objects ◽

Radar Data ◽

Synthetic Aperture ◽

Moving Targets ◽

Lp Norm ◽

Radar Imagery ◽

Sar Imagery ◽

Norm Constraint ◽

A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.

Better Utilization of SAR Dynamic Range

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/107118137702100314 ◽

1977 ◽

Vol 21 (3) ◽

pp. 235-240

Author(s):

Edward J. Dragavon

Keyword(s):

Computer Simulation ◽

Real Time ◽

Image Enhancement ◽

Dynamic Range ◽

Synthetic Aperture ◽

Analytic Methods ◽

Sar Imagery ◽

Gray Shade ◽

Three general classes of image enhancement techniques for synthetic aperture radar (SAR) video were investigated through non-real-time computer simulation. The general categories were 1) monochromatic adaptive gray shade transformations, 2) pseudocolor encoding, and 3) feature analytic methods. The class of feature analytic techniques was found to have the greatest potential for improving the operational utility of SAR imagery.