Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification

• URL: http://arxiv.org/abs/2006.15771v1
• Date: Mon, 29 Jun 2020 01:40:54 GMT
• Title: Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification
• Authors: Sheng-Jie Liu, Haowen Luo, Qian Shi
• Abstract summary: In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classification. We show that only 35% of the predicted labels of a deep learning model's snapshots near its convergence were exactly the same. 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.
• Score: 10.80252725670625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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