Sea ice can cause some of the most prominent marine disasters in polar and high latitude regions, and remote sensing technology provides an important means to detect such hazards. The accuracy of sea ice detection depends on the number and quality of labeled samples, but because of environmental conditions in sea ice regions, acquisition of labeled samples can be time-consuming and labor-intensive. To solve this problem, we propose a combined active learning (AL) and semisupervised learning (SSL) classification framework for sea ice detection. At first, we acquire most informative and representative samples by AL; then labeled samples acquired by AL are used as the initial labeled samples for SSL, in this framework, we not only choose the most valuable samples but also use the large number of unlabeled samples to enhance the classification accuracy. In AL phase, we use two different sampling strategies: uncertainty and diversity. In the SSL phase, we utilize a sampling function integrating AL to acquire semilabeled samples, and we use a transductive support vector machine as a classification model. We analyze three remote sensing images (hyperspectral and multispectral) and conduct detailed comparative analyses between the proposed method and others. Our proposed method achieves the highest classification accuracies (89.9734%, 97.4919%, and 89.7166%) in both experiments. These results show that the proposed method exhibits better overall performance than other methods and can be effectively applied to sea ice detection using remote sensing.