# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 3 The Overall Idea of Our Method  
In object detection in remote sensing images, template matching-based methods are simple and easy to implement, but the template design becomes more and more complicated when directions and shapes of objects vary greatly. Knowledge-based object detection methods can gain better detection performance through abundant a priori knowledge, but how to define the a priori knowledge and rules is a hard problem, which usually requires much experience. While machine learning-based object detection methods are based on shallow feature extraction methods, such as HOG, SIFT, and other classic features presented in object detection on regular images, and have a better detection effect for some specific scenes, when the remote sensing background is complex and the objects are diversified, the scalability of these methods is poor. Deep learning has a great advantage in automatically learning deep-level features, but it is still at a relatively early stage for object detection in remote sensing images. Meanwhile deep learning requires a large amount of labeled data, which are less available in remote sensing images. Aiming at these shortcomings, we propose to employ abundant labeled regular image datasets to assist the object detection in the remote sensing images through the transfer learning method and explore the validity of the transfer learning. As shown in Figure 1, from the detection process, the framework of the proposed approach can be divided into three steps: rapid candidate region proposal, deep feature extraction of the candidate window, context scene feature fusion, classification, and post-processing. The training process is mainly to train the models used in the steps of the detection stage: (a) train the candidate region proposal model; (b) combine transfer learning to train the deep feature extraction network; (c) combine transfer learning to train the context scene feature extraction network; (d) train the classification model.  
In particular, we improve the stages of candidate region proposal and feature extraction:  
1. For the stage of candidate region proposal, by analyzing the particularity of the remote sensing image object detection task, we select the BING algorithm to improve it by integrating multiple weak feature scoring to extend to large-scale images. The experiment shows that the improved algorithm achieves a better detection rate and more accurate object coverage when obtaining the same number of candidate windows.
2. For the stage of feature extraction, we employ CNN to extract deep features of the candidate windows and the windows' context scene, respectively, and then fuse the two kinds of features for detection, which improves the detection performance. In addition, we solve the problem of the insufficient annotations on remote sensing images by transfer learning, which reduces the risk of over-fitting and improves the network's ability of feature expression on remote sensing objects and scenes.  
In the stage of classification and post-processing, we employ the faster linear SVM with the hard negative mining method to reduce the impact of overfull negative samples. Finally, we filter out duplicates by the non-maximum suppression algorithm to further optimize the test results.  
Figure 1: Proposed object detection framework.  
In the following sections, we mainly discuss the two stages: candidate region proposal and feature extraction, and analyze the results of the experiments respectively.