Automatic detection of internal wave using particle swarm optimization algorithm

Tandem-X satellite data application to four-dimensional of copper mineralization

“…particle ( , , , m n l t ) denotes a probable solution to the optimization problem. Consistent with Kennedy & Eberhart 19 and Marghany 14 , , ,…”

“…The following sections explain this approach clearly. Succeeding Marghany, 14 Particle Swarm Optimization (PSO) is a population-based randomly investigation process. It is assumed that there are N "particles" i.e., lineaments, faults, topographic breaks, bedding, depressions, lithologies, which are in SAR data.…”

“…Following Kennedy & Eberhart 22 and El Meseery et al, 23 Marghany, 14 the error threshold is performed, the optimal solution is obtained post accomplishing a unique quantity of iterations or an accurate.…”

“…The correct DEMs are necessary for geological, mining detections in spite of the negative aspects of synthetic aperture radar records due to speckles and object geometry distortions. Lopes et al, 5 Touzi, 6 Yu & Scott, 7 Hondt et al, 8 Helmy & El-Taweel, 9 Marghany, 10 Marghany [10][11][12][13] Recently, Marghany 14 developed a new technique for geological, mining detection in TerraSAR-X based Particle Swarm Optimization (PSO). In this view, Marghany 14 and Marghany 15 mentioned that Particle Swarm Optimization has accurate performance in solving several single and multi-objective optimization noises in TerraSAR-X data, such as noise despeckles, which approved by Riccardo et al, 16 and Jin et al 17 This optimization of SAR speckles might produce high target visibility in coherence data.…”

“…Lopes et al, 5 Touzi, 6 Yu & Scott, 7 Hondt et al, 8 Helmy & El-Taweel, 9 Marghany, 10 Marghany [10][11][12][13] Recently, Marghany 14 developed a new technique for geological, mining detection in TerraSAR-X based Particle Swarm Optimization (PSO). In this view, Marghany 14 and Marghany 15 mentioned that Particle Swarm Optimization has accurate performance in solving several single and multi-objective optimization noises in TerraSAR-X data, such as noise despeckles, which approved by Riccardo et al, 16 and Jin et al 17 This optimization of SAR speckles might produce high target visibility in coherence data. This investigation has hypothesized that 4-D phase unwrapping through the use of Particle Swarm Optimization (PSO) can be used to reconstruct four-dimensional of copper mineralization from the TanDEM-X data.…”