公开(公告)号：US20220414376A1

公开(公告)日：2022-12-29

申请号：US17358850

申请日：2021-06-25

Applicant: Raytheon Company

Inventor： Grant B. Boroughs ,  John J. Coogan ,  Lisa A. McCoy

IPC: G06K9/00 ,  G06K9/62

Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

公开(公告)号：US11941878B2

公开(公告)日：2024-03-26

申请号：US17358850

申请日：2021-06-25

Applicant: Raytheon Company

Inventor： Grant B. Boroughs ,  John J. Coogan ,  Lisa A. McCoy

IPC: G06V20/58 ,  G06V10/75 ,  G06V20/10 ,  G06V10/58

CPC classification number: G06V20/182 ,  G06V10/751 ,  G06V20/176 ,  G06V20/584 ,  G06V10/58 ,  G06V20/194

Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.