Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study

Abstract

Support vector machine (SVM) algorithms have been widely utilized in the remote sensing community due to their high performance with small training datasets. While previous research has indicated that incorporating mixed pixels into training can enhance the performance of SVM, the impact of the percentage of mixed pixels on classification accuracy remains unexplored. Furthermore, the combined effects of this percentage with other factors including training size, kernel functions (linear, polynomial, radial basis function, and sigmoid), and regularization, have not been thoroughly examined. To address these gaps, this study utilized simulated remote sensing imagery and its corresponding reference map to systematically analyze the impact of these factors on SVM classification accuracy. The results indicate that when the regularization parameter is greater than 1, including mixed pixels in the training generally reduces accuracy, except when a polynomial kernel is used. In contrast, with a lower regularization parameter (<1), at least 50 mixed pixels per class are required in the training dataset to achieve a robust improvement in accuracy. Within these conditions, accuracy increases substantially with a training size up to 300 and a mixed pixel percentage up to 40%. Beyond these thresholds, adding more mixed pixels or training samples leads to minor gains in accuracy. These findings underscore the importance of optimizing the proportion of mixed pixels and carefully selecting regularization parameters to maximize SVM performance in remote sensing applications.

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