Extraction of themes from aerial imagery using latent dirichlet allocation
Shailesh Shankar Deshpande, Shamsuddin Ladha, Hemant Kumar Aggarwal, Piyush Yadav · 2017
This paper presents the generative image processing model for extracting themes from aerial imagery. We model image as a collection of themes and each theme as a collection of objects. We learn urban themes using unsupervised Latent Dirichlet Allocation. Further, we use the learned topic (theme) model directly to infer some of the important parameters of facility management. On a test dataset of the port city of Zeebruges, our approach successfully identified open container parking lot, occupied parking lot, and open spaces in urban areas. Overall theme accuracy of our approach is about 83%.