Raytheon Company

Michael J Sand

Peter Kim

Matthew D Hollenbeck

Jason Reis Chaves

Ryan Quiller

Mark S Berlin

A computing machine receives a real synthetic aperture radar (SAR) image including one or more targets. The real SAR image is one of a plurality of real SAR images in a training set. The computing machine generates, for the real SAR image, a model-based target shadow background (TSB) image using a three-dimensional (3D) model of the target. The computing machine generates, for the real SAR image and using an auto-encoder engine, an auto-encoder-generated TSB image using an artificial neural network (ANN). The computing machine computes, using a discriminator engine, an image difference between the auto-encoder-generated TSB image and the model-based TSB image. The computing machine adjusts weights in the auto-encoder engine based on the computed image difference.

Generative adversarial network-based target identification

A computer architecture for artificial image generation is disclosed. According to some aspects, a computing machine receives a voxel model of a target object. The target object is to be recognized using an image recognizer. The computing machine generates, based on the voxel model, a set of TSB (target shadow background-mask) images of the target object. The computing machine receives, at an auto-encoder, a set of real images of the target object. The computing machine generates, using an auto-encoder and based on the set of real images, one or more artificial images of the target object based on the set of TSB images. The computing machine provides, as output, the generated one or more artificial images of the target object.

Computer architecture for artificial image generation

A computer architecture for artificial image generation is disclosed. According to some aspects, a computing machine receives a voxel model of a first set of objects different from a target object. The target object is to be recognized using an image recognizer. The computing machine generates, based on the voxel model, a set of TSB (target shadow background-mask) images of the first set of objects. The computing machine receives, at an auto-encoder, a set of real images of the first set of objects. The computing machine generates, using the auto-encoder, one or more artificial images of the target object based on the set of TSB images. The auto-encoder learns differences between the target object and the first set of objects. The computing machine provides, as output, the generated one or more artificial images of the target object.

Computer architecture for artificial image generation using auto-encoder

A machine trains a first neural network using a first set of images. Training the first neural network comprises computing a first set of weights for a first set of neurons. The machine, for each of one or more alpha values in order from smallest to largest, trains an additional neural network using an additional set of images. The additional set of images comprises a homographic transformation of the first set of images. The homographic transformation is computed based on the alpha value. Training the additional neural network comprises computing an additional set of weights for an additional set of neurons. The additional set of weights is initialized based on a previously computed set of weights. The machine generates a trained ensemble neural network comprising the first neural network and one or more additional neural networks corresponding to the one or more alpha values.

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Torrance, CA, United States of America

Michael J Sand