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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Deep Fusion of Remote Sensing Data for Accurate Classification
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Deep Fusion of Remote Sensing Data for Accurate Classification

机译：深度融合遥感数据以进行准确分类

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摘要

The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of remote sensing data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future remote sensing data fusion.
机译：近年来，遥感数据的多传感器融合备受关注。在这封信中，我们提出了一个基于深度神经网络（DNN）的新特征融合框架。所提出的框架采用深度卷积神经网络（CNN）来有效地提取多光谱/高光谱以及光检测和测距数据的特征。然后，设计一个完全连接的DNN以融合以前的CNN获得的异构特征。通过上述深层网络，可以提取遥感数据的判别和不变特征，这对于进一步处理很有用。最后，将逻辑回归用于产生最终分类结果。深度融合框架中采用了辍学和批处理规范化策略，以进一步提高分类准确性。所得结果表明，所提出的深度融合模型在分类准确性方面提供了有竞争力的结果。此外，提出的深度学习思想为将来的遥感数据融合打开了一个新窗口。

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