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Remote Sens. 2015, 7(4), 4651-4677; doi:10.3390/rs70404651

Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery

1
Instituto Universitario del Agua y del Medio Ambiente, Universidad de Murcia, Edificio D Campus de Espinardo s/n, 30100 Murcia, Spain
2
Departamento de Geología y Minas e Ingeniería Civil, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja, Ecuador
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 11 February 2015 / Revised: 28 March 2015 / Accepted: 8 April 2015 / Published: 17 April 2015
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Abstract

Object-based image analysis allows several different features to be calculated for the resulting objects. However, a large number of features means longer computing times and might even result in a loss of classification accuracy. In this study, we use four feature ranking methods (maximum correlation, average correlation, Jeffries–Matusita distance and mean decrease in the Gini index) and five classification algorithms (linear discriminant analysis, naive Bayes, weighted k-nearest neighbors, support vector machines and random forest). The objective is to discover the optimal algorithm and feature subset to maximize accuracy when classifying a set of 1,076,937 objects, produced by the prior segmentation of a 0.45-m resolution multispectral image, with 356 features calculated on each object. The study area is both large (9070 ha) and diverse, which increases the possibility to generalize the results. The mean decrease in the Gini index was found to be the feature ranking method that provided highest accuracy for all of the classification algorithms. In addition, support vector machines and random forest obtained the highest accuracy in the classification, both using their default parameters. This is a useful result that could be taken into account in the processing of high-resolution images in large and diverse areas to obtain a land cover classification. View Full-Text
Keywords: random forest; feature selection; object-based image analysis; Hughes effect; photogrammetric camera; classification random forest; feature selection; object-based image analysis; Hughes effect; photogrammetric camera; classification
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

Cánovas-García, F.; Alonso-Sarría, F. Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery. Remote Sens. 2015, 7, 4651-4677.

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