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.