Classification Performance Evaluation of Deep Learning Architectures for Complex Object Based Facility Recognition

Nuclear proliferation monitoring using remote sensing imagery is an essential area of research from a national security perspective. Studies have shown that traditional per-pixel and small patch based classification schemes prove to be insufficient in detecting complex facilities such as coal, gas and nuclear. In this paper, we evaluate the state of the art deep learning architectures for detecting the aforementioned complex facilities. We use the principles of transfer learning to fine-tune the state of the art deep learning architectures using models pre-trained on 1000 classes of Imagenet data. We evaluated the classification performance of these models on a benchmark imagery database consisting of two hundred complex facilities collected over the United States.