# 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
### Deep Feature Extraction and Context Scene Feature Fusion  
As objects in remote sensing images have a strong background context, for example, the airplane objects may appear on the runway, parking apron, but not in the forest, the port, etc., and the airplanes may be side by side with another one, how to describe this a priori knowledge and apply them to the detection algorithm comprise a difficult problem. In order to utilize contextual information, conventional algorithms usually define structure and matching constraints, but this manual approach is too subjective and not extensible to different problems. In this paper, we extract the scene context feature of the candidate window and fuse this feature with the candidate window feature to classify, while making the classifier automatically learn the constraint between the object and the context scene. In Section 2, it is indicated that the feature extraction based on CNN can avoid the unmanageability and subjectivity of the manual design feature and can obtain a deeper feature representation of the object. Therefore, the feature of the object window and the context scene are both extracted from CNN. For the convenience of description, we named the two networks as objNetand seeNet. In the training process, we trained objNet and seeNet respectively. In the testing process, as shown in Figure 11, we extracted the feature of the candidate window, this being the context scene by the corresponding network, and then fused the two kinds of features to classify. For the feature fusion, taking into account the detection rate, we simply merged the two features and used the faster linear SVM classification. Figure 10: Airplane objects in high-resolution remote sensing images. Figure 9: Example of the context scene of a car in regular images. For the scene context of the candidate region, as shown in Figure 12, we extended the candidate region from the center point and obtained a 256 \(\times\) 256 pixel region as its context scene. When the object was at the edge of the image, we made the scene bounding box a minimum translation, so that it did not exceed the image area. For the seeNet network, we used the classic AlexNet network structure, and modified the final output layer number. As shown in Figure 13, the network consisted of eight layers, of which the first five layers were the convolution layer, which can be regarded as multi-stage feature extraction. The latter three layers were fully-connected layers. The parameters of each layer are shown in Table 3. In the detection process, the scene bounding box of the candidate region was input to the network for feed forward calculation, and the output of the fifth layer was used as the scene feature of the candidate region. Figure 11: Window feature and context scene feature fusion. Figure 12: The definition of the context scene for the candidate region. Figure 13: seeNet network structure. For the feature extraction of the candidate window, the structure of the objNet network is shown in Figure 14. Taking into account that the airplane object is very small and the sizes are more concentrated in 64 \(\times\) 64 pixels, so the network input layer using 64 \(\times\) 64, other sizes of candidate windows need to be scaled. Since the object to be detected in this paper is only an airplane, the output of the network is two classes: airplane or background. Compared with the classification of the remote sensing scene using AlexNet, the input size of the objNet network was smaller, and the outputs were fewer, so the network can be considered to need relatively simple feature representation when performing object discrimination. Therefore, when designing the objNet network, we modified the size of the convolution and pool layer windows and reduced the number of convolution cores and neurons in the fully-connected layer. The simplified network had fewer parameters and could reduce the risk of over-fitting properly. However, this does not mean that the network was as simple as possible.