A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

Li, Huapeng; Zhang, Shuqing; Zhang, Ce; Li, Ping; Cropp, Roger Allan

doi:10.1080/01431161.2017.1368102

Cited by 22 publications

(11 citation statements)

References 61 publications

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“…The "speed-displacement" search strategy, changing particle's position with iterations by adjusting v i , has been employed to select the band subset. Compared with the traditional evolutionary strategy, the "speed-displacement" search strategy of PSO is more effective [38,39]. Moreover, BS-PSO simultaneously introduces the synergy and competition into the searching process for optimization.…”

Section: Discussionmentioning

confidence: 99%

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

Ding

et al. 2020

Open Geosciences

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Swarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys–Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms’ performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA’s, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

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Section: Discussionmentioning

confidence: 99%

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

Ding

et al. 2020

Open Geosciences

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“…The Levy flight can improve the performance of natureinspired algorithms [31]. A new solution x tþ1 can be obtained through the Levy flight:…”

Section: Discrete Levy Flightmentioning

confidence: 99%

Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times

Zhang

Liu

2019

Adv. Manuf.

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The type-II mixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.

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“…Research on land use could help us to optimize land development and utilization. In land use research, many methods have been proposed to improve accuracy of land use classification 27–32 . CART (Classification and Regression Trees) algorithm is one of the decision tree classification algorithms widely used in land use classification 33,34 .…”

Section: Introductionmentioning

confidence: 99%

Intensity and Stationarity Analysis of Land Use Change Based on CART Algorithm

Sang

Guo

et al. 2019

Sci Rep

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Land use directly reflects degree of human development and utilization of land. Intensity analysis of land use is a quantitative method to analyze land use changes. In this paper, land use changes in Tianjin were studied using Thematic Mapper (TM) remote sensing images in 1995, 2000, 2005, 2010 and Operational Land Imager (OLI) remote sensing image in 2015. Land use information was extracted using decision tree classification method based on CART (Classification and Regression Trees) algorithm. This paper introduced land use intensity analysis to analyze its change intensity and stationarity, respectively at interval, category and transition levels. Based on the theory, new models were developed in the transition level to analyze land use change pattern. The analysis quantifies the contribution of a certain land categories to land use change during a specific time interval. The change of land use during 1995–2015 indicated that Tianjin experienced rapid urban development with the area of urban land increased by about 7.5%. This study provided a reference for the sustainable development of land use in Tianjin.

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A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

Cited by 22 publications

References 61 publications

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times

Intensity and Stationarity Analysis of Land Use Change Based on CART Algorithm

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