US 11,656,353 B2
Object measurement using deep learning analysis of synthetic aperture radar backscatter signatures
Xin Li, Santa Clara, CA (US); and Matthew Dunseth Wood, Palo Alto, CA (US)
Assigned to Orbital Insight, Inc., Palo Alto, CA (US)
Filed by Orbital Insight, Inc., Palo Alto, CA (US)
Filed on Oct. 10, 2019, as Appl. No. 16/599,036.
Prior Publication US 2021/0109209 A1, Apr. 15, 2021
Int. Cl. G01S 13/90 (2006.01); G01S 7/41 (2006.01)
CPC G01S 13/9027 (2019.05) [G01S 7/417 (2013.01)] 19 Claims
OG exemplary drawing
1. A method for processing synthetic aperture radar (SAR) signatures from a SAR device, the method comprising:
receiving an SAR backscatter image representing an SAR backscatter signature of a geographical area including an object of interest at a given time, the SAR backscatter signature including a two dimensional array of intensity values;
modifying the SAR backscatter image by removing portions of the SAR backscatter image until the object of interest is within a threshold value of a center of the image;
extracting one or more features from the modified SAR backscatter image based on intensity values of the modified SAR backscatter image;
inputting the one or more features into a neural network model, the neural network model trained using training data, the training data including one or more training sets, each training set comprising labeled SAR backscatter signatures of objects of interest;
receiving, as an output from the neural network model, coordinate values indicating one or more visual features of the object of interest; and
determining one or more measurements of the object of interest based on the coordinate values, wherein each of the one or more measurements characterize a state of the object of interest at the given time.