Fusing two convolutional neural networks for high-resolution scene classification
Xiaoyong Bian, Chen Chen, Yuxia Sheng, Yan Xu, Qian Du · 2017
This paper presents a novel deep convolutional feature fusion (ConvFF) approach for high-resolution scene classification, characterizing the well-known deep convolutional neural network (ConvNet) approach. The proposed ConvFF approach starts by generating an initial feature representation of the original scenes under exploration from two deep ConvNets pre-trained on two different large amount of labeled data. After the pre-training phase, we fine tune the two deep ConvNets consisting of mainly objects and scenes respectively in a supervised manner using the target training images. Then we propose to fuse the extracted two types of convolutional features provided by the last fully-connected (FC) layer, respectively. Finally, the fused convolutional features are fed as input to a SVM classifier for classification. The proposed method is evaluated by using two challenging high-resolution scene datasets. Experimental results show that the proposed method can effectively extract complementary features of the scenes and capture local spatial patterns, consistently outperforming several state-of-the-art methods.