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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification
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Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification

机译：Polariemetric合成孔径雷达图像分类有效集合深度学习

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摘要

Although deep learning has achieved great success in the image-classification tasks, its performance is subject to the quantity and quality of the training samples. For the classification of the 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 the deep-learning model's snapshots near its convergence were exactly the same. The disagreement between the snapshots is nonnegligible. From the perspective of multiview learning, the snapshots together serve as a good committee to evaluate the importance of the unlabeled instances. Using the snapshot committee to give out the informativeness of the unlabeled data, the proposed AEDL achieved better performance on two real PolSAR images than the standard active learning strategies. It achieved the same classification accuracy with only 86% and 55% of the training samples compared to the breaking tie active learning and random selection for the Flevoland data set.
机译：虽然深入学习在图像分类任务中取得了巨大成功，但其性能受培训样本的数量和质量。对于偏振型合成孔径雷达（POLSAR）图像的分类，几乎不可能从视觉解释中注释图像。因此，遥感科学家迫切需要在很少的训练样本的情况下开发POLSAR图像分类的新技术。在这封信中，我们采取了积极学习的优势，并为Polsar图像分类提出了积极的集成深度学习（AEDL）。我们首先表明，在其融合附近的深度学习模型的快照中只有35％的预测标签完全相同。快照之间的分歧是不可缩牌的。从多视图学习的角度来看，快照将作为一个良好的委员会，以评估未标记的实例的重要性。使用快照委员会透露未标记数据的信息，提议的AEDL在两个真正的Polsar图像上实现了比标准的主动学习策略更好的性能。与Butvoland数据集的断裂主动学习和随机选择相比，它达到了相同的分类准确性，只有86％和55％的训练样本。

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