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Remote Sens. 2017, 9(8), 868; https://doi.org/10.3390/rs9080868

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

1,2,* , 1,2,* , 3
,
1,2,* , 4
and
1,2
1
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
College of Computer Science, China University of Geosciences, Wuhan 430074, China
4
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Received: 26 June 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 22 August 2017
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Abstract

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification. View Full-Text
Keywords: data fusion; hyperspectral; LiDAR; differential evolution; decision fusion; land-use/land-cover classification data fusion; hyperspectral; LiDAR; differential evolution; decision fusion; land-use/land-cover classification
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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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Zhong, Y.; Cao, Q.; Zhao, J.; Ma, A.; Zhao, B.; Zhang, L. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data. Remote Sens. 2017, 9, 868.

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