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Selective Data Augmentation Approach for Remote Sensing Scene Classification

机译：遥感场景分类的选择性数据增强方法

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Scene classification in remote sensing (RS) images is an issue that attracted a lot of researchers' attention recently. Using CNN for scene classification has been investigated in depth. One difficulty in using CNN models in remote sensing is the limited amount of data. Data augmentation techniques have been shown to provide one solution to this problem, yet, few works have investigated these techniques in their methods. In this work, our main contribution is presenting a novel method for selective data augmentation in remote sensing. The proposed selective augmentation method tries to optimize the way we augment the training set. We do that by being selective in the new scenes that we generate and add to the training set. This will help us achieve the best results with the least amount of training data added. The method selects scenes based on a “quality” criterion. To that end we investigate two criteria for evaluating the quality of new scenes namely, one based on entropy and another one known as the breaking-ties criterion. The initial results present promising capabilities of this solution for four RS scene datasets in enhancing the accuracy of classification.
机译：遥感（RS）图像中的场景分类是一个最近引起了很多研究人员关注的问题。已经对使用CNN进行场景分类进行了深入研究。在遥感中使用CNN模型的一个困难是有限的数据量。数据增强技术已显示出可以解决此问题的一种方法，但是，很少有作品在其方法中研究过这些技术。在这项工作中，我们的主要贡献是提出了一种用于遥感中选择性数据增强的新方法。拟议的选择性扩充方法试图优化我们扩充训练集的方式。我们通过在生成的新场景中进行选择并添加到训练集中来做到这一点。这将帮助我们以最少的培训数据量获得最佳结果。该方法基于“质量”标准选择场景。为此，我们研究了两种评估新场景质量的标准，一种是基于熵的标准，另一种是所谓的平局标准。初步结果表明，该解决方案对四个RS场景数据集的解决方案有望提高分类的准确性。

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