期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 15, 期 2, 页码 183-186出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2779469
关键词
1 x 1 convolution; composite convolutional neural networks (CNNs); converted CNN; deep feature fusion; global average pooling (GAP)
类别
资金
- National Natural Science Foundation of China [61673184]
In convolutional neural networks (CNNs), higher layer information is more abstract and more task specific, so people usually concern themselves with fully connected (FC) layer features, believing that lower layer features are less discriminative. However, a few researchers showed that the lower layers also provide very rich and powerful information for image representation. In view of these study findings, in this letter, we attempt to adaptively and explicitly combine the activations from intermediate and FC layers to generate a new CNN with directed acyclic graph topology, which is called the converted CNN. After that, two converted CNNs are integrated together to further improve the classification performance. We validate our proposed two-stage deep feature fusion model over two publicly available remote sensing data sets, and achieve a state-of-the-art performance in scene classification tasks.
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