SYNTHETIC AUGMENTATION METHODS FOR OBJECT DETECTION IN OVERHEAD IMAGERY

The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective--—e.g., target resolution, imaging band(s), and imaging angle--—test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we have developed a spin-set augmentation method that leverages synthetic data generation capabilities to augment the training data sets. We then test this method with the popular object detection networks YOLO, SSD, and Faster R-CNN. This thesis analyzes the synthetic augmentation method in terms of algorithm detection performance, computational complexity, and generalizability.