SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Li, M. Q.; Xu, Liyun; Xu, Na; Huang, Tao; Yan, Baixing

doi:10.1155/2018/4576015

Cited by 16 publications

(7 citation statements)

References 44 publications

(44 reference statements)

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“…The omega wolves are the scapegoats of the pack, they have to submit to all the other dominant wolves. The deltas have to submit to alphas and betas, but they dominate the omegas [23]. The rank of the wolves equals the fitness of the solutions.…”

Section: Preliminariesmentioning

confidence: 99%

A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Yan

Zhao

et al. 2019

Sensors

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A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

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Section: Preliminariesmentioning

confidence: 99%

A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Yan

Zhao

et al. 2019

Sensors

Self Cite

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“…In GWO, the search direction is determined by the best three wolves and followed by other candidate wolves. It has been successfully applied for solving optimization problems in the fields of flow shop scheduling [25][26][27], computer science [28][29][30], mathematics [31][32][33], water resources [34][35][36], energy [37][38][39], and so on. But this kind of exploitation mechanism makes it prone to stagnation in a local optimum in multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Zhao

Wang

et al. 2020

Energies

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Construction duration and schedule robustness are of great importance to ensure efficient construction. However, the current literature has neglected the importance of schedule robustness. Relatively little attention has been paid to schedule robustness via deviation of an activity’s starting time, which does not consider schedule robustness via structural deviation caused by the logical relationships among activities. This leads to a possibility of deviation between the planned schedule and the actual situation. Thus, an optimization model of construction duration and schedule robustness is proposed to solve this problem. Firstly, duration and two robustness criteria including starting time deviation and structural deviation were selected as the optimization objectives. Secondly, critical chain method and starting time criticality (STC) method were adopted to allocate buffers to the schedule in order to generate alternative schedules for optimization. Thirdly, hybrid grey wolf optimizer with sine cosine algorithm (HGWOSCA) was proposed to solve the optimization model. The movement directions and speed of grey wolf optimizer (GWO) was improved by sine cosine algorithm (SCA) so that the algorithm’s performance of convergence, diversity, accuracy, and distribution improved. Finally, an underground power station in China was used for a case study, by which the applicability and advantages of the proposed model were proved.

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“…This method is affected by the noise and the image contrast conditions. Fuzzy C-means clustering method is an unsupervised technique that segregates the objects into several populations based on their qualities; the choice of initial cluster centers is done in a random fashion and this heavily influences the solution [14][15][16]. Due to this random selection, there is a possibility of delay in convergence rate or there will be a chance of getting stuck in local optimal solution [15], which leads to the increase of computing time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Jing

et al. 2019

Mathematical Problems in Engineering

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Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

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SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Cited by 16 publications

References 44 publications

A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Optimization of Construction Duration and Schedule Robustness Based on Hybrid Grey Wolf Optimizer with Sine Cosine Algorithm

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

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