# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 5 Deep Feature Extraction with Context Scene Feature Fusion and Detection
### Results and Analysis
#### 5.5.3 Result Analysis  
In order to prove the validity of the transfer learning, this section firstly gives the classification accuracy of the object and scene feature extraction network in the training process and then gives the influence of the feature extraction on the detection effect before and after the transfer learning. Furthermore, the effectiveness of scene feature fusion is illustrated by contrasting the detection performance before and after the context scene feature fusion. Finally, we compare the other algorithms to prove the validity of the proposed remote sensing object detection algorithm based on deep learning with scene feature fusion. Because the sceNet network uses the classic AlexNet network structure, there are many trained parameter models based on the network, which can be used for transfer learning. During the training process, the parameter models of AlexNet, CaffeNet, Places205-AlexNet, and Hybrid-AlexNet were used for transfer learning in this paper. AlexNet and CaffeNet were trained on the ImageNet2012 dataset, and CaffeNet has a very similar architecture to AlexNet, except for two small modifications: training without data augmentation and exchanging the order of pooling and normalization layers. Places205-AlexNet is a parameter model trained on the Places dataset. Hybrid-AlexNet was trained on the dataset combining the Place dataset with the ImageNet2012 dataset for a total of 3.6 million images in 1183 categories. Table 5 shows the classification accuracy of the remote sensing scene using  
Figure 17: Examples of the NATURE-PLANE dataset used in the transfer learning of the objNet network. different transfer learning models, where \"AlexNet-RSI\" indicates the learned network model without transfer learning and \"xx-TL\" denotes the network transferred from different models. Table 5 reveals that the accuracy of the network trained with transfer learning was much higher than that of a network trained directly using remote sensing scene data. After transfer learning, the accuracy of the Hybrid-AlexNet network trained on the Place and ImageNet 2012 datasets was the highest, so we used the Hybrid-AlexNet-TL model to extract the feature of the object context scene. In addition, by visualizing the convolution kernel parameters of the first convolution layer, as shown in Figure 18, this shows that the convolution kernel of the network with transfer learning learned more edge features, and without transfer learning, the first layer of the network simply learned some simple fuzzy color information. For the training of object classification network objNet, because the network structure was designed in this paper, there was no trained model for transfer learning, so it was necessary to pre-train the transferable model parameters. Therefore, we used the NATURE-PLANE dataset introduced in Section 5.4.1 to pre-train objNet. However, in practice, it appears that if we pre-train the objNet directly using ImageNet2012's complete data and resume training using the NATURE-PLANE dataset, a better classification result could be obtained. Table 6 lists the classification accuracy of different pre-trained objNet networks, where objNet-RSI denotes the network model obtained without using transfer learning. After the training of the objNet network and seeNet network was complete, we used the two networks for feature extraction and classification detection. We first compared the detection performance before and after using transfer learning in objNet when there was no scene feature fusion. Then, to fuse the scene features, in the fusion, we also compared the effect of seeNet before and after transfer. That is, there was in total four groups of experiments, and the four groups of experiments had a progressive relationship, in which the fourth set of experiments was our proposed algorithm. In order to simplify the following description, we named each experiment and the configuration of each set of experiments as listed in Table 7.