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

Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
College of Precision Instrument and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
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;
Received: 8 April 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 19 October 2016
PDF [28642 KB, uploaded 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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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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Zhou, G.; Zhang, R.; Zhang, D. Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification. Remote Sens. 2016, 8, 855.

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