Fusion object detection of satellite imagery with arbitrary-oriented region convolutional neural network

Object detection on multi-source images from satellite platforms is difficult due to the characteristics of imaging sensors. Multi-model image fusion provides a possibility to improve the performance of object detection. This paper proposes a fusion object detection framework with arbitrary-oriented region convolutional neural network. First, nine kinds of pansharpening methods are utilized to fuse multi-source images. Second, a novel object detection framework based on Faster Region-based Convolutional Neural Network structure is used, which is suitable for large-scale satellite images. Region Proposal Network is adopted to generate axially aligned bounding boxes enclosing object sin different orientations, and then extract features by pooling layers with different sizes. These features are used to classify the proposals, adjust the bounding boxes, and predict the inclined boxes and the objectness/non-objectness score. Smaller anchors for small objects are considered. Finally, inclined non-maximum suppression method is utilized to get the detection results. Experimental results showed that the proposed method performs better than some state-of-the-art object detection techniques, such as YOLO-v2, YOLO-v3, etc. Some numerical tests validate the efficiency and effectiveness of the proposed method. © 2019, Shanghai Jiao Tong University.