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A novel multi-parameter support vector machine for image classification

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>7/04/2015
<mark>Journal</mark>International Journal of Remote Sensing
Issue number7
Number of pages17
Pages (from-to)1890-1906
Publication StatusPublished
<mark>Original language</mark>English


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 paper, 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 fresh water ecosystem in northeast China. A high overall accuracy of 82.19% with Kappa 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 Kappa 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 Kappa of 0.80 compared with an overall accuracy 76.05% and Kappa coefficient 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.