Geospatial intelligence is a subject with many opportunities for machine automation. Object detection is one desirable application. However, a lack of high-volume relevant datasets can make this task difficult. To combat this issue, we introduced a spin-set augmentation technique to generate synthetic training data. We used these synthetic datasets to augment the training of an object detection deep network, focusing on visible band imagery. We have continued our efforts by further testing this method on long-wave infrared imagery, including results from YOLO, SSD, and Faster R-CNN algorithms. We also introduce another synthetic augmentation technique which involves generating physics-based fully-rendered images of 3D synthetic scenery and targets and compared the rendered image performance to that of spin-sets. This paper analyzes both the spin-set and rendered image augmentation techniques in terms of object detection performance, complexity, generalizability, and explainability.
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 deep network YOLOv4. This paper analyzes the synthetic augmentation method in terms of algorithm detection performance, computational complexity, and generalizability.
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