# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 2 Related Works  
The development of object detection in high-resolution remote sensing images has been in three stages: template matching-based, knowledge testing-based, and machine learning-based. [PERSON] et al. [1] performed template matching by extending the hit-or-miss transformation in a multivariate fashion to detect storage tanks and the shoreline. [PERSON] et al. [2] demonstrated the detection of urban buildings by using the SIFT feature to represent a two-building template. Knowledge testing-based object detection transforms detection into the hypothesis testing problem by establishing a set of knowledge and rules. [PERSON] et al. [3] used buildings and shadows to detect buildings of arbitrary shape automatically. Machine learning-based methods have been the main direction of object detection in the remote sensing field. For example, [PERSON] et al. [4] described the airplane objects by a set of key point SIFT feature and a graph model to detect them. [PERSON] et al. [5] constructed a bag-of-words model by clustering SIFT feature points to represent targets and classified them by SVM. [PERSON] et al. [6] performed ship detection on remote sensing images by extracting the HOG feature of the sliding window and extracted the continuous window feature by rotating the sliding window to achieve certain rotation invariance. [PERSON] et al. [7] clustered the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable by a template matching approach. [PERSON] et al. [8] automatically selected a representative subset of texture features by the AdaBoost algorithm and used them to identify airport runways and then detect the airport. [PERSON] et al. [9] obtained good results in general object detection by combining the pyramid HOG feature with the partial deformation model and training an SVM with hidden variables. Some scholars have applied weak-supervised object detection algorithms to remote sensing images [10]. However, these methods mostly relied on a variety of well-designed prior knowledge or shallow features, etc. [11; 12]. That is, they require a wealth of experience and a tedious trial and error process, and they have limitations. In the field of object detection on regular images, [PERSON] et al. [13] proposed an image classification algorithm based on deep learning, which automatically extracts the higher level features of the images by a convolutional neural network (CNN), which made the image classification task more concise and the classification accuracy significantly better. Thanks to the development of deep learning and the development of region proposal algorithms, the object detection field has also made great breakthroughs. One of the main challenges of object detection in remote sensing images is how to reduce the computational complexity. In view of the large scale of remotely-sensed images, there will be a large number of candidates if the conventional multi-scale scanning window exhaustive strategy is used to obtain the region of interest, which makes the subsequent feature extraction and classification cost too much to achieve fast detection. Therefore, how to reduce the search space in this field is a key problem. In recent years, many region proposal algorithms have been proposed [14; 15; 16; 17]. These algorithms can be divided into two categories:(1) segmentation and combination methods: divide the input image into fragments and then combine these fragments by some bottom-up strategy to generate regions of interest; (2) the window scoring method: define the scoring criteria of the probability of the candidate window containing the object, scoring each possible window by the sliding window method, and selecting the candidate with a high score. There are two more regularly-used region proposal algorithms: the selective search algorithm [14] and the binarized normed gradients (BING) algorithm [15]. In 2014, [PERSON] et al.