Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy of image scene classification in such systems. To classify the scene from areal images, we used a two-stream deep architecture. We performed the first part of the classification, the feature extraction, using pre-trained CNN that extracts deep features of aerial images from different network layers: the average pooling layer or some of the previous convolutional layers. Next, we applied feature concatenation on extracted features from various neural networks, after dimensionality reduction was performed on enormous feature vectors. We experimented extensively with different CNN architectures, to get optimal results. Finally, we used the Support Vector Machine (SVM) for the classification of the concatenated features. The competitiveness of the examined technique was evaluated on two real-world datasets: UC Merced and WHU-RS. The obtained classification accuracies demonstrate that the considered method has competitive results compared to other cutting-edge techniques.

中文翻译：

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深度学习用于遥感中的特征提取：空中场景分类的案例研究。

在许多与遥感有关的系统和应用中，依赖于图像的场景分类至关重要。对从远程收集的图像进行场景分类的科学兴趣正在增加，并且正在开发许多数据集和算法。卷积神经网络（CNN）和其他深度学习技术的引入极大地提高了此类系统中图像场景分类的准确性。为了根据区域图像对场景进行分类，我们使用了两流深度架构。我们使用预先训练的CNN进行分类的第一部分，即特征提取，该方法从不同的网络层（平均池化层或某些先前的卷积层）提取航空图像的深层特征。下一个，在对巨大的特征向量进行降维后，我们将特征级联应用于从各种神经网络提取的特征。我们对不同的CNN架构进行了广泛的实验，以获得最佳结果。最后，我们使用支持向量机（SVM）对串联特征进行分类。在两个真实的数据集上评估了所研究技术的竞争力：UC Merced和WHU-RS。获得的分类精度表明，与其他先进技术相比，该方法具有竞争优势。我们使用支持向量机（SVM）对串联特征进行分类。在两个真实的数据集上评估了所研究技术的竞争力：UC Merced和WHU-RS。获得的分类精度表明，与其他先进技术相比，该方法具有竞争优势。我们使用支持向量机（SVM）对串联特征进行分类。在两个真实的数据集上评估了所研究技术的竞争力：UC Merced和WHU-RS。获得的分类精度表明，与其他先进技术相比，该方法具有竞争优势。