公开(公告)号：US20240221113A1

公开(公告)日：2024-07-04

申请号：US18149416

申请日：2023-01-03

Applicant: BAE Systems Information and Electronic Systems Integration Inc.

Inventor： Bingcai Zhang

IPC: G06T3/40

CPC classification number: G06T3/4053 ,  G06T3/4046

Abstract: System and methods for generating super resolution images from geospatial images having any number of bands. A super resolution model, which uses deep convolution neural networks (DCNNs), is trained using individual image bands, a large crop size or tile size of 512×512 pixels, and a de-noise algorithm. Applying one or more algorithms to maintain the original color of the image bands improves the quality metrics of the super resolution images as measured by PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) of super resolution images. Further applying one or more algorithms to remove border effects introduced during the disclosed process reduces and/or eliminates seam lines between tiles and enhances the overall accuracy of the super resolution images.

公开(公告)号：US10346720B2

公开(公告)日：2019-07-09

申请号：US15806383

申请日：2017-11-08

Applicant: BAE Systems Information and Electronic Systems Integration Inc.

Inventor： Bingcai Zhang

IPC: G06K9/00 ,  G06K9/62 ,  G06T3/60 ,  G06N3/08 ,  G06N3/063

Abstract: System and method for detecting objects in geospatial images, 3D point clouds and Digital Surface Models (DSMs). Deep Convolution Neural Networks (DCNNs) are trained using positive and negative training examples. Using a rotation pattern match of only positive examples reduces the number of negative examples required. In DCNNs softmax probability is variant of rotation angles. When rotation angle is coincident with object orientation, softmax probability has maximum value. During training, positive examples are rotated so that their orientation angles are zero. During detection, test images are rotated through different angles. At each angle, softmax probability is computed. A final object detection is based on maximum softmax probability as well as a pattern match between softmax probability patterns of all positive examples and the softmax probability pattern of a target object at different rotation angles. The object orientation is determined at the rotation angle when softmax probability has maximum value.

公开(公告)号：US20190138849A1

公开(公告)日：2019-05-09

申请号：US15806383

申请日：2017-11-08

Applicant: BAE Systems Information and Electronic Systems Integration Inc.

Inventor： Bingcai Zhang

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/063 ,  G06K9/00 ,  G06T3/60

CPC classification number: G06K9/6256 ,  G06K9/0063 ,  G06K9/00973 ,  G06K9/6202 ,  G06K9/6215 ,  G06K9/623 ,  G06K2209/21 ,  G06N3/063 ,  G06N3/08 ,  G06T3/60

Abstract: System and method for detecting objects in geospatial images, 3D point clouds and Digital Surface Models (DSMs). Deep Convolution Neural Networks (DCNNs) are trained using positive and negative training examples. Using a rotation pattern match of only positive examples reduces the number of negative examples required. In DCNNs softmax probability is variant of rotation angles. When rotation angle is coincident with object orientation, softmax probability has maximum value. During training, positive examples are rotated so that their orientation angles are zero. During detection, test images are rotated through different angles. At each angle, softmax probability is computed. A final object detection is based on maximum softmax probability as well as a pattern match between softmax probability patterns of all positive examples and the softmax probability pattern of a target object at different rotation angles. The object orientation is determined at the rotation angle when softmax probability has maximum value.