Image Classification Using SVMs: One-against-One Vs One-against-All

TL;DR

This paper compares one-against-one and one-against-all SVM approaches for land cover classification, finding similar accuracy but different pixel classification characteristics, and concludes the choice depends on dataset specifics.

Contribution

It evaluates the impact of 1A1 and 1AA SVM approaches on land cover mapping, providing insights into their practical differences and implications.

Findings

1AA yields more unclassified and mixed pixels

Classification accuracy is similar for both methods

Choice depends on dataset and user preference

Abstract

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusion therefore that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.