Integration of fuzzy theory and particle swarm optimization for high-resolution satellite scene recognition

2017 ◽  
Vol 7 (2) ◽  
pp. 147-154
Author(s):  
Linyi Li ◽  
Yun Chen ◽  
Tingbao Xu
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Fuzzy Theory ◽  
Particle Swarm ◽  
Scene Recognition ◽  
Swarm Optimization

scholarly journals Gaussian Qualitative Trigonometric Functions in a Fuzzy Circle

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
M. Clement Joe Anand ◽  
Janani Bharatraj
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Circle ◽  
Fuzzy Theory ◽  
Particle Swarm ◽  
Membership Functions ◽  
Trigonometric Functions ◽  
Swarm Optimization ◽  
Qualitative Representation ◽  
Trigonometric Identities

We build a bridge between qualitative representation and quantitative representation using fuzzy qualitative trigonometry. A unit circle obtained from fuzzy qualitative representation replaces the quantitative unit circle. Namely, we have developed the concept of a qualitative unit circle from the view of fuzzy theory using Gaussian membership functions, which play a key role in shaping the fuzzy circle and help in obtaining sharper boundaries. We have also developed the trigonometric identities based on qualitative representation by defining trigonometric functions qualitatively and applied the concept to fuzzy particle swarm optimization using α-cuts.

scholarly journals Video Superresolution via Parameter-Optimized Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yunyi Yan ◽  
Yujie He ◽  
Yingying Hu ◽  
Baolong Guo
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Video Sequence ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Objective Standard ◽  
Objective Performance ◽  
Multiple Swarms

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.

scholarly journals Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

2020 ◽  
Vol 12 (20) ◽  
pp. 3302
Author(s):  
Lei Yu ◽  
Chunsheng Li ◽  
Jie Chen ◽  
Pengbo Wang ◽  
Zhirong Men
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Global Convergence ◽  
Particle Swarm ◽  
Sar Images ◽  
Processing Efficiency ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Imaging Results ◽  
Phase Signal

To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO.

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