Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 8, Pages 1253-1257Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2704625
Keywords
Convolutional neural network (CNN); data fusion; deep neural network (DNN); feature extraction (FE); hyperspectral image (HSI); light detection and ranging (LiDAR); multispectral image (MSI)
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Funding
- State Key Laboratory of Frozen Soil Engineering [SKLFSE201614]
- Natural Science Foundation of China [61301206, 61371180, 60972144]
- National Science Foundation for Excellent Young Scholars [61522107]
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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.
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