A novel multi-parameter support vector machine for image classification

Abstract

The support vector machine (SVM) classification algorithm has received increasing attention in recent years in remote sensing for land-cover classification. However, it is well known that the performance of the SVM is sensitive to the choice of parameter settings. The traditional single optimized parameter SVM (SOP-SVM) attempts to identify globally optimized parameters for multi-class land-cover classification. In this article, a novel multi-parameter SVM (MP-SVM) algorithm is proposed for image classification. It divides the training set into several subsets, which are subsequently combined. Based on these combinations, sub-classifiers are constructed using their own optimum parameters, providing votes for each pixel with which to construct the final output. The SOP-SVM and MP-SVM were tested on three pilot study sites with very high, high, and low levels of landscape complexity within the Sanjiang Plain – a typical inland wetland and freshwater ecosystem in northeast China. A high overall accuracy of 82.19% with kappa coefficient (κ) of 0.80 was achieved by the MP-SVM in the very high-complexity landscape, statistically significantly different (z-value = 3.77) from the overall accuracy of 72.50% and κ of 0.69 produced by the traditional SOP-SVM. Besides, for the moderate-complexity landscape a significant increase in accuracy was achieved (z-value = 2.44), with overall accuracy of 84.03% and κ of 0.80 compared with an overall accuracy 76.05% and κ of 0.71 for the SOP-SVM. However, for the low-complexity landscape the MP-SVM was not significantly different from the SOP-SVM (z-value = 0.80). Thus, the results suggest that the MP-SVM method is promising for application to very high and high levels of landscape complexity, differentiating complex land-cover classes that are spectrally mixed, such as marsh, bare land, and meadow.

Acknowledgements

The authors are grateful to the staff of the Honghe National Nature Reserve in Heilongjiang Province, China, for their kind assistance during the field surveys. The authors thank Andrew MacLachlan (Geography and Environment, University of Southampton, UK) for his valuable help in checking the grammar and sentence structure. The authors also thank the two anonymous referees for their constructive comments on this manuscript.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 41271196] and the European Union Erasmus Mundus Scholarship [grant number 2011-0155].