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Sensors 2016, 16(8), 1204; doi:10.3390/s16081204

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

1,2
,
1,2,* and 2,3,*
1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
3
Academy of Disaster Reduction and Emergency Management Ministry of Civil and Ministry of Education, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Jason K. Levy
Received: 10 June 2016 / Revised: 20 July 2016 / Accepted: 25 July 2016 / Published: 30 July 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [9778 KB, uploaded 30 July 2016]   |  

Abstract

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. View Full-Text
Keywords: feature selection; particle swarm optimization; change detection; remote sensing feature selection; particle swarm optimization; change detection; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, Q.; Chen, Y.; Jiang, W. Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection. Sensors 2016, 16, 1204.

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