Classification of Images Using Support Vector Machines

TL;DR

This paper evaluates the effectiveness of Support Vector Machines (SVMs) for land cover classification, comparing One-Against-One and One-Against-All approaches, and finds similar accuracy despite different pixel classification behaviors.

Contribution

It provides an empirical comparison of 1A1 and 1AA SVM approaches for land cover mapping, highlighting their similarities and differences.

Findings

1AA yields more unclassified and mixed pixels.

Classification accuracy is similar for both approaches.

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 conclusions that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.