US 12,333,798 B2
Convolutional neural network (CNN) for automatic target recognition in a satellite
Manuel Gonzalez-Rivero, Alexandria, VA (US); David R. Herdzik, Ypsilanti, MI (US); and Jonathan C. Harris, Ellicott City, MD (US)
Assigned to Maxar Space LLC, Palo Alto, CA (US)
Filed by Maxar Space LLC, Palo Alto, CA (US)
Filed on Mar. 16, 2022, as Appl. No. 17/696,709.
Prior Publication US 2023/0298341 A1, Sep. 21, 2023
Int. Cl. G06V 20/13 (2022.01); G06N 3/048 (2023.01); G06T 1/60 (2006.01)
CPC G06V 20/13 (2022.01) [G06N 3/048 (2023.01); G06T 1/60 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A satellite, comprising:
an image sensor configured to generate image data of a two dimensional array of pixel values; and
an automatic target recognition circuit configured to receive the image data and comprising:
a plurality of N processing nodes each configured to apply a convolutional neural network (CNN) to a subset of pixels values of the image data received from the image sensor; and
one or more control circuits configured to:
receive the image data from the image sensor;
separate the image data into a plurality of N non-overlapping subsets of the image data, each of the subsets comprising pixel values of a plurality of contiguous pixel locations, the combined pixel locations of the N subsets containing all pixel locations of the image data;
process in parallel each of the N subsets of the image data in a corresponding one of the processing nodes by applying the processing node's CNN to the corresponding subset of the image data;
combine results of the processing of each of the N subsets of the image data in a corresponding one of the processing nodes to obtain a combined output for the image data; and
determine whether a target is recognized based upon the combined output for the image data.