A Modified Classification Algorithm of MCSM/H-PSO of Fully Polarimetric SAR Image

doi:10.13474/j.cnki.11-2246.2018.0349

测绘通报 ›› 2018, Vol. 0 ›› Issue (11): 53-57.doi: 10.13474/j.cnki.11-2246.2018.0349

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A Modified Classification Algorithm of MCSM/H-PSO of Fully Polarimetric SAR Image

YU Shasha¹, YU Jie^1,2,3,4, ZHU Teng⁵, WANG Yanbing^1,2,3

1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
2. State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China;
3. Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China;
4. Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China;
5. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China

Received:2018-01-28 Revised:2018-04-23 Online:2018-11-25 Published:2018-11-29

Abstract

Abstract:

Particle swarm optimization (PSO) is widely used in image classification because of the ability of random global optimization.The quality of particle has a major influence on the identification of cluster center.Because of the spatial correlation of adjacent pixels in fully polarimetric SAR image,we proposed to use weighted PSO algorithm in SAR image classification to make the cluster center more reasonable for the improvement of classification accuracy.Meanwhile,we used the multiple-component scattering model (MCSM) method combined with the scatter entropy to extract the 6 polarization features of the images,making full use of the polarimetric characteristics of the polarimetric SAR images.In the proposed method,firstly,the SAR image is classified as preliminary classificat based on scattering mechanism by MCSM decomposition and scatter entropy. Secondly,the result of classification is used as the initial classification of the weighted PSO algorithm to achieve the classification of objects by iteration.The result of using the full polarimetric SAR data of Radarsat2 in Beijing and the full polarimetric SAR data of AIRSAR in San Francisco AIRSAR show that the total accuracy of the proposed method was 90.57% and 93.25%,respectively.

Key words: SAR image classification, MCSM decomposition, the PSO algorithm, scattering entropy

CLC Number:

P237

YU Shasha, YU Jie, ZHU Teng, WANG Yanbing. A Modified Classification Algorithm of MCSM/H-PSO of Fully Polarimetric SAR Image[J]. 测绘通报, 2018, 0(11): 53-57.

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A Modified Classification Algorithm of MCSM/H-PSO of Fully Polarimetric SAR Image

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