Deep learning-based object level change detection in overhead imagery

Adam G. Francisco; Matthew D. Reisman; Jon J. Dalrymple; Kevin J. LaTourette

doi:10.1117/12.2587080

12 April 2021 Deep learning-based object level change detection in overhead imagery

Adam G. Francisco, Matthew D. Reisman, Jon J. Dalrymple, Kevin J. LaTourette

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290N (2021) https://doi.org/10.1117/12.2587080
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Change detection between two temporal scenes of overhead imagery is a common problem in many applications of computer vision and image processing. Traditional change detection techniques only provide a pixel level detail of change and are sensitive to noise and variations in images such as lighting, season, perspective. We propose a deep learning approach that exploits a segmentation detector and classifier to perform object level change detection. This allows us to create class level segmentation masks of a pair of images collected from the same location at different times. This pair of segmentation masks can be compared to detect altered objects, providing a detailed report to a user on which objects in a scene have changed.

Conference Presentation

Citation Download Citation

Adam G. Francisco, Matthew D. Reisman, Jon J. Dalrymple, and Kevin J. LaTourette "Deep learning-based object level change detection in overhead imagery", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290N (12 April 2021); https://doi.org/10.1117/12.2587080

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