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

Understand

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.