  Although deep learning has achieved great success in image classification
tasks, its performance is subject to the quantity and quality of training
samples. For classification of polarimetric synthetic aperture radar (PolSAR)
images, it is nearly impossible to annotate the images from visual
interpretation. Therefore, it is urgent for remote sensing scientists to
develop new techniques for PolSAR image classification under the condition of
very few training samples. In this letter, we take the advantage of active
learning and propose active ensemble deep learning (AEDL) for PolSAR image
classification. We first show that only 35\% of the predicted labels of a deep
learning model's snapshots near its convergence were exactly the same. The
disagreement between snapshots is non-negligible. From the perspective of
multiview learning, the snapshots together serve as a good committee to
evaluate the importance of unlabeled instances. Using the snapshots committee
to give out the informativeness of unlabeled data, the proposed AEDL achieved
better performance on two real PolSAR images compared with standard active
learning strategies. It achieved the same classification accuracy with only 86%
and 55% of the training samples compared with breaking ties active learning and
random selection for the Flevoland dataset.
