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Sensors 2017, 17(5), 960; doi:10.3390/s17050960

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

1
Department of Geography, Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China
2
Department of Resources Science and Technology, Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China
3
Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 22 February 2017 / Revised: 3 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
View Full-Text   |   Download PDF [5322 KB, uploaded 26 April 2017]   |  

Abstract

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C) and kernel width (s), in mapping homogeneous specific land cover. View Full-Text
Keywords: support vector data description; optimal parameters; window-based validation set; simulated annealing; land cover support vector data description; optimal parameters; window-based validation set; simulated annealing; land cover
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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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MDPI and ACS Style

Zhang, J.; Yuan, Z.; Shuai, G.; Pan, Y.; Zhu, X. Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set. Sensors 2017, 17, 960.

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