System and method for transferring electro-optical (EO) knowledge for synthetic-aperture-radar (SAR)-based object detection

A computer implemented method for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain, the method comprising an act of:

• causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
• using a measured similarity between the EO domain and the SAR domain, training a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain,
• wherein Sliced-Wasserstein Distance (SWD) is used to determine the measured similarity,
• wherein SWD is used to minimize a discrepancy between an EO domain probability distribution and a SAR domain probability distribution in a latent space of a neural auto-encoder in order to supervise training of domain-specific encoders;
• using the trained model, processing a SAR image to determine regions of interest in the SAR image;
• classifying a region of interest to determine whether the region of interest corresponds to an object of interest;
• outputting classified regions of interest that contain the object of interest;
• displaying the object of interest on a visualization map;
• and
• automatically updating the visualization map to reflect a change in position of the object of interest.

A computer program product for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain, the computer program product comprising:

• computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
• using a measured similarity between the EO domain and the SAR domain, training a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain,
• wherein Sliced-Wasserstein Distance (SWD) is used to determine the measured similarity,
• wherein SWD is used to minimize a discrepancy between an EO domain probability distribution and a SAR domain probability distribution in a latent space of a neural auto-encoder in order to supervise training of domain-specific encoders;
• using the trained model, processing a SAR image to determine regions of interest in the SAR image;
• classifying a region of interest to determine whether the region of interest corresponds to an object of interest;
• outputting classified regions of interest that contain the object of interest;
• displaying the object of interest on a visualization map;
• and
• automatically updating the visualization map to reflect a change in position of the object of interest.

A system for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain, the system comprising:

• one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform an operation of:
• using a measured similarity between the EO domain and the SAR domain, training a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain;
• training the model with a set of labeled data points in the EO domain;
• and
• adapting the trained model using a set of labeled data points in the SAR domain, where a number of data points in the set of labeled data points in the SAR domain is significantly fewer than a number of data points in the set of labeled data points in the EO domain,
• wherein the set of labeled data points in the SAR domain and a set of unlabeled data points in the SAR domain are used to align an EO probability distribution and a SAR domain probability distribution;
• using the trained model, processing a SAR image to determine regions of interest in the SAR image;
• classifying a region of interest to determine whether the region of interest corresponds to an object of interest;
• outputting classified regions of interest that contain the object of interest;
• displaying the object of interest on a visualization map;
• and
• automatically updating the visualization map to reflect a change in position of the object of interest.

A computer implemented method for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain, the method comprising an act of:

• causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
• using a measured similarity between the EO domain and the SAR domain, training a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain;
• training the model with a set of labeled data points in the EO domain;
• and
• adapting the trained model using a set of labeled data points in the SAR domain, where a number of data points in the set of labeled data points in the SAR domain is significantly fewer than a number of data points in the set of labeled data points in the EO domain,
• wherein the set of labeled data points in the SAR domain and a set of unlabeled data points in the SAR domain are used to align an EO probability distribution and a SAR domain probability distribution;
• using the trained model, processing a SAR image to determine regions of interest in the SAR image;
• classifying a region of interest to determine whether the region of interest corresponds to an object of interest;
• outputting classified regions of interest that contain the object of interest;
• displaying the object of interest on a visualization map;
• and
• automatically updating the visualization map to reflect a change in position of the object of interest.

A computer program product for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain, the computer program product comprising:

• computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
• using a measured similarity between the EO domain and the SAR domain, training a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain;
• training the model with a set of labeled data points in the EO domain;
• and
• adapting the trained model using a set of labeled data points in the SAR domain, where a number of data points in the set of labeled data points in the SAR domain is significantly fewer than a number of data points in the set of labeled data points in the EO domain,
• wherein the set of labeled data points in the SAR domain and a set of unlabeled data points in the SAR domain are used to align an EO probability distribution and a SAR domain probability distribution;
• using the trained model, processing a SAR image to determine regions of interest in the SAR image;
• classifying a region of interest to determine whether the region of interest corresponds to an object of interest;
• outputting classified regions of interest that contain the object of interest;
• displaying the object of interest on a visualization map;
• and
• automatically updating the visualization map to reflect a change in position of the object of interest.