Using the VENμS Super-Spectral Camera for detecting moving vehicles

Monitoring transportation for planning, management, and security purposes has become a growing interest for various stakeholders. A methodology for detecting moving vehicles is based on the acquisition time gap between the pushbroom detector sub-arrays. However, this technique requires overcoming differences in ground sampling distance and/or spectral features of the sensor's bands used for change detection. The current work demonstrates a proof of concept for the VEN mu S satellite's capability to detect moving vehicles in a single pass with a relatively low spatial resolution. The VEN mu S Super-Spectral Camera has a unique stereoscopic capability because the two spectral bands, with the same central wavelength and width, are positioned at the extreme ends of the camera's focal plane. This design enables a 2.7-sec difference in observation time. The normalized difference moving object index (NDMOI) has been designed to detect moving vehicles using these bands without image preprocessing for dimensionality reduction or geometric corrections, as other sensors require. Results show the successful detection of small-to medium-sized moving vehicles. Especially interesting is the detection of private cars that are, on average, 2-3 m smaller than the VEN mu S ground sampling distance. Vehicle movement was effectively detected in different backgrounds/environments, e.g., on asphalt and unpaved roads, as well as over bare soil and plowed fields. Furthermore, a multitemporal analysis of moving vehicles during the Covid-19 pandemic in 2020 shows the effectiveness of the proposed methodology.

Automated summary

This study demonstrates the concept validation of using Venus microsatellites with relatively low spatial resolution to detect moving vehicles in a single pass. The unique stereoscopic capability of the microsatellite's super-spectral camera enables the detection of moving vehicles without preprocessing or geometric corrections. The study shows successful detection of small to medium-sized vehicles and proves the effectiveness of the proposed methodology during the Covid-19 pandemic in 2020.