Dr. Bingcai Zhang Profile

Dr. Bingcai Zhang

at BAE Systems

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Publications (4)

Proceedings Article | 4 October 2023 Presentation + Paper

Maintaining original color when applying super-resolution to individual bands

Bingcai Zhang, Yen Luu

Proceedings Volume 12675, 1267504 (2023) https://doi.org/10.1117/12.2670602

KEYWORDS: Super resolution, Object detection, Satellites, Satellite imaging, Image resolution, Earth observing sensors, Image processing, Image quality

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Proceedings Article | 6 June 2022 Presentation + Paper

Deducing 15 cm detail from 30 cm satellite images with deep learning

Bingcai Zhang, Yen Luu, Kalyan Vaidyanathan, Fidel Paderes, Reuben Settergren, Kurt de Venecia

Proceedings Volume 12113, 1211310 (2022) https://doi.org/10.1117/12.2622690

KEYWORDS: Super resolution, Image resolution, Satellites, Satellite imaging, Earth observing sensors, Image analysis, Multispectral imaging, Image enhancement, Feature extraction

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Proceedings Article | 13 April 2021 Presentation + Paper

Intelligent photogrammetry for digital elevation model production

Bingcai Zhang

Proceedings Volume 11729, 1172902 (2021) https://doi.org/10.1117/12.2595802

KEYWORDS: Photogrammetry, 3D modeling, Unmanned aerial vehicles, Satellite imaging, Machine learning

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Digital Elevation Model (DEM) production is one of the most time consuming tasks in digital photogrammetry. By applying machine learning to Digital Photogrammetry, our Intelligent Photogrammetry can significantly reduce the cost of DEM production from Digital Surface Models (DSM), which are generated from satellite images, aerial images, Unmanned Aerial Vehicle (UAV) images, sparse LiDAR 3-D point clouds, and dense LiDAR 3-D point clouds. There are various types of DSM, each containing different post spacing and accuracy. The following sets of 3-D models have been trained based on the different DSM types: 1. 3DLargeBuildingModel 2. 3DBuildingModel 3. 3DHouseModel 4. 3DTreeModel 5. 3DGroundPointModel The first four models detect above ground 3-D objects and then remove them from DSM to generate DEM. The last model classifies 3-D points into thirteen categories, which are then used to generate DEM in difficult terrain such as dense forestry areas, where the ground is mostly unseen. The main cost of DEM production using DSM generated from satellite images in difficult terrain is the transformation from DSM to DEM. Traditional handcrafted bare earth algorithms for DSM to DEM transformation cannot deal with so many different cases for general purpose application and big data. Intelligent Photogrammetry, based on machine learning, can handle different cases by adding training samples. For this case study, the city of San Diego was used to generate DEM from Intelligent Photogrammetry to achieve Root Mean Square Error (RMSE) of 0.95 meters from stereo satellite images. This case study indicates that Intelligent Photogrammetry can reduce the DEM production cost by more than 50%. The most time consuming component of DEM production is dense forestry areas, and in this case study, the forestry height is up to 19 meters causing the ground to be nearly invisible. This issue was resolved with 3DGroundPointModel based on machine learning, achieving RMSE 2.40 meter and meeting the desired DEM accuracy requirement of 2 to 3 meters using stereo satellite images. DEM production from UAV images using our Intelligent Photogrammetry can achieve state-of-the-art accuracy. The Intelligent Photogrammetry can identify errors in DSM generated from UAV images and correct them; therefore, providing a very competitive DEM generation capability for UAV images.

Proceedings Article | 14 May 2019 Presentation + Paper

Fast and accurate target detection in overhead imagery using double convolution neural networks

Bingcai Zhang, Riad Hammoud

Proceedings Volume 10988, 109880O (2019) https://doi.org/10.1117/12.2519795

KEYWORDS: Target detection, Convolution, Neural networks, Feature extraction, Convolutional neural networks, Detection and tracking algorithms, Automatic target recognition

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