# 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  
### Feature Extraction Algorithm CNN  
After obtaining the candidate windows that may contain objects, we needed to further determine whether the windows actually contained objects, that is the detection task can be converted into the classification task. To classify the candidate windows, the distinguishing features of each window need to be extracted first. As a base problem of the computer vision field, many classic feature extraction methods have been proposed, such as color histogram, gray level co-occurrence matrix, Haar-like features, HOG features, and SIFT features. These features were designed by scholars according to experience and related prior knowledge and have achieved good results in many fields. However, at the ImageNet LSVRC-2010 competition, [PERSON] et al. [13] used deep learning to extract features automatically, reducing the top-5 error rate of the image classification task by 15.3%, far beyond the algorithms with traditional feature extraction methods. The reason is that the traditional feature representation can only obtain the shallow features of the image, and it has certain limitations; however, deep learning can automatically learn higher level feature representation from the original image, which has better distinguishing ability and versatility. Deep learning is developing rapidly, and the most regularly used in the field of object detection is the convolutional neural network (CNN). Compared with traditional neural networks, CNN achieves weight sharing by introducing the convolution layer, which makes the network structure sparser and reduces the complexity of the model. The convolutional algorithm can obtain the feature map of different aspects by difficult convolution kernel parameters. The convolution operation makes the obtained feature map have translation invariance. The convolution layer is usually followed by a pooling layer, which downsamples the obtained feature map layer, preserving useful information while reducing the amount of data to be further processed. The pooling layer usually uses max-pooling to get the maximum response of local features, so that the obtained features have better rotation and light invariance. CNN can learn a higher level feature representation from low-level features through a deep network structure by stacking multiple convolution layers. For different objects, the learned low-level features by CNN differ slightly, which are usually some edge information, and through multi-layer network learning, abstract features of different objects can be obtained finally. After the multiple convolution layer, a fixed-length feature vector is obtained through the full-connection layer and output to the classifier. Generally, the softmax classifier is employed in the  
Figure 8: Average running time of the BING algorithm and the pBING algorithm on different datasets. CNN, and it outputs the probability that the image belongs to each class. CNN combines feature learning with the classification task, which makes the extracted feature more task related. Model of Neurons  
CNN is a type of multi-layer sensor for which each layer is composed of a two-dimensional plane, and each plane is composed of various independent neurons. In the network, some simple and complex cell are marked as cell \(C\) and cell \(S\), which is inspired by the vision concept from biology. In the visual cortex, there are two kinds of related cells, simple ones (cell \(S\)) and complex ones (cell \(C\)). Cell \(S\) responds to the stimulation for the modes like margins of images in its maximum receptive field, while cell \(C\) has a bigger receptive field, which can locate the modes of stimulation in a spatial way. The merging of \(C\) cells forms convolutional layers, denoted as \(U_{C}\), while the merging of \(S\) cells forms the downsampling layers, which can be denoted as \(U_{S}\). Any intermediate layer in the network is composed of the \(S\)-layer and \(C\)-layer with series connection.