Investigation of search strategies to identify optimized and efficient templates for automatic target recognition in remotely sensed imagery

Object detection remains an important and ever-present component of computer vision applications. While deep learning has been the focal point for much of the research actively being conducted in this area, there still exists certain applications in which such a sophisticated and complex system is not required. For example, if a very specific object or set of objects are desired to be automatically identified, and these objects' appearances are known a priori, then a much simpler and more straightforward approach known as matched filtering, or template matching, can be a very accurate and powerful tool to employ for object detection. In our previous work, we investigated using machine learning, specifically, the improved Evolution COnstructed features framework, to identify (near-) optimal templates for matched filtering given a specific problem. Herein, we explore how different search algorithms, e.g., genetic algorithm, particle swarm optimization, gravitational search algorithm, can derive not only (near-) optimal templates, but also promote templates that are more efficient. Specifically, given a defined template for a particular object of interest, can these search algorithms identify a subset of information that enables more efficient detection algorithms while minimizing degradation of detection performance. Performance is assessed in the context of algorithm efficiency, accuracy of the object detection algorithm and its associated false alarm rate, and search algorithm performance. Experiments are conducted on handpicked images of commercial aircraft from the xView dataset | one of the largest publicly available datasets of overhead imagery.