Target detection and identification are well-studied problems in the visible and near infrared (IR) bands, with recent work focusing on the short wave IR (SWIR) band. The extended SWIR (eSWIR) band (2 to 2.5 μm) offers an advantage over SWIR due to increased atmospheric transmission, while keeping greater diffraction-limited angular resolution than the midwave IR and longwave IR. eSWIR should additionally improve object-sky contrast due to having lower background sky path radiance than the SWIR. An analysis of the signal-to-noise ratio and contrast for drone imaging in the reflective bands is presented and compared with a Night Vision Integrated Performance Model of drone detection performance using equivalent reflectivities. We find that imaging performance across all four bands is strongly dependent on pixel pitch and contrast.
KEYWORDS: Reflectivity, Short wave infrared radiation, Near infrared, Signal to noise ratio, Cameras, Sensors, Target detection, Atmospheric particles, Visual process modeling, Performance modeling
Target detection and identification are well-studied problems in the visible (VIS) and near infrared (NIR) bands, with recent work focusing on the short wave IR (SWIR) band. The extended SWIR (eSWIR) band (2 to 2.5 μm) offers an advantage over SWIR due to increased atmospheric transmission, while keeping greater angular resolution than the midwave and longwave IR. eSWIR should additionally improve object-sky contrast due to lower background sky path radiance than the SWIR. An analysis of drone signal-to-noise ratio (SNR) and contrast in the reflective bands is presented and compared to an NVIPM model of drone detection performance using equivalent reflectivities.
The recent advancements in commercial drone performance and capability have seen their use in private industries proliferate. In terms of large area coverage, low-flying drones can accomplish the same tasks as larger unmanned aerial vehicles (UAVs) and small manned aircraft. Traditional methods of capturing this imagery, including single wide field of view (WFOV) cameras and gimbal-mounted systems, can be replaced by small camera arrays. Single WFOV lenses deliver poor resolution at the ground level. Similarly, the use of a narrow field of view (NFOV) lens would necessitate the use of a gimbal, a pivoted support used in camera stabilization – yielding a heavier, more expensive system that relies on additional moving parts. By utilizing multiple lightweight sensors, large area coverage while maintaining good ground sample resolution can be achieved as well as promise a more robust system. This paper will explore the creation and testing of one such system, describe a means by which more advanced systems can be developed, and introduce a metric so as to compare its performance against various modeled systems.
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