Abstract: Most image classification methods require adequate labeled training samples to train classifier models. In real world applications, labelling samples are often very time consuming and expensive, especially for some special images, e.g. synthetic aperture radar (SAR) images. So the number of labeled samples is usually limited. In this study, we propose a novel image classification method based on SVMs, incorporating best vs second-best (BvSB) active learning and constrained self-training (CST). In this method, BvSB active learning is used to explore examples that are the most valuable to current classifier model for manual labelling. And CST is used to exploit useful information from examples that remain in the unlabeled dataset. With this new method, satisfying classification performance can be achieved while the human labelling load is low. We demonstrate results on 3 optical image datasets and a SAR image dataset. The proposed method gives large reduction in the number of human labeled samples as compared with random selection, entropy based active learning and BvSB active learning to achieve similar classification accuracy, and has little computational overhead and good robustness.