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Optimized extreme learning machine for urban land cover classification using hyperspectral imagery |
Hongjun SU1( ), Shufang TIAN2( ), Yue CAI1, Yehua SHENG3,4, Chen CHEN5, Maryam NAJAFIAN5 |
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China 2. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China 3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China 4. Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China 5. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080-3021, USA |
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Abstract This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficientC and Gaussian kernel s for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.
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Keywords
extreme learning machine
firefly algorithm
parameters optimization
hyperspectral image classification
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Corresponding Author(s):
Hongjun SU,Shufang TIAN
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Just Accepted Date: 12 October 2016
Online First Date: 04 November 2016
Issue Date: 10 November 2017
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