Non-parametric spatio-temporal activity learning from overhead imagery

Cem Safak Sahin; Evan Koester; Zhongheng Li; Evan Bouillet

doi:10.1117/12.2626167

31 May 2022 Non-parametric spatio-temporal activity learning from overhead imagery

Cem Safak Sahin, Evan Koester, Zhongheng Li, Evan Bouillet

Proceedings Volume 12096, Automatic Target Recognition XXXII; 1209602 (2022) https://doi.org/10.1117/12.2626167
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Analysis of imagery derived from low-earth orbiting satellites has a long history going back into the midst of Cold War era. For a long time, these imagery have been provided by military satellites and mainly used by the Department of Defense (dod) and Intelligence Community (ic) analysts. Since the mid of 1990s, the international constellation of commercial satellites has been growing with increasing temporal and spatial resolutions such as Maxar constellation currently provides nearly four million square kilometers per day translating into a staggering 100TB of imagery every day. These satellites thus enable tremendous opportunities for various government and commercial tasks. In this paper, we present our software framework combining state-of-the-art object detection and change identification algorithms with statistical learning techniques to detect various objects-of-interest (permanent- and semipermanent-structures and vehicles) and learn their behaviors. Our approach is applicable for detecting both macro- and micro- scale changes by turning vast amount of imagery collected by commercial satellites into information and information into actionable insight.

Conference Presentation

Citation Download Citation

Cem Safak Sahin, Evan Koester, Zhongheng Li, and Evan Bouillet "Non-parametric spatio-temporal activity learning from overhead imagery", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209602 (31 May 2022); https://doi.org/10.1117/12.2626167

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