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Article

Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification

by 1,2,3,*, 2,3,* and 3
1
Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
2
College of Precision Instrument and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
3
The Center for Remote Sensing, Tianjin University, Tianjin 300072, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Norman Kerle, Soe Myint, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2016, 8(10), 855; https://doi.org/10.3390/rs8100855
Received: 8 April 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 19 October 2016
Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs. View Full-Text
Keywords: decision tree; co-location; MVU; imagery classification decision tree; co-location; MVU; imagery classification
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MDPI and ACS Style

Zhou, G.; Zhang, R.; Zhang, D. Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification. Remote Sens. 2016, 8, 855. https://doi.org/10.3390/rs8100855

AMA Style

Zhou G, Zhang R, Zhang D. Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification. Remote Sensing. 2016; 8(10):855. https://doi.org/10.3390/rs8100855

Chicago/Turabian Style

Zhou, Guoqing, Rongting Zhang, and Dianjun Zhang. 2016. "Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification" Remote Sensing 8, no. 10: 855. https://doi.org/10.3390/rs8100855

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