Dr. Huawu Deng Profile

Dr. Huawu Deng

Technical Architect at PCI Geomatics

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Publications (2)

Proceedings Article | 15 October 2014 Paper

Building high-performance system for processing a daily large volume of Chinese satellite imagery

Huawu Deng, Shicun Huang, Qi Wang, Zhiqiang Pan, Yubin Xin

Proceedings Volume 9247, 924708 (2014) https://doi.org/10.1117/12.2064087

KEYWORDS: Satellites, Satellite imaging, Image processing, Computing systems, Distributed computing, Earth observing sensors, Parallel computing, Geomatics, Data processing, Mathematical modeling

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The number of Earth observation satellites from China increases dramatically recently and those satellites are acquiring a large volume of imagery daily. As the main portal of image processing and distribution from those Chinese satellites, the China Centre for Resources Satellite Data and Application (CRESDA) has been working with PCI Geomatics during the last three years to solve two issues in this regard: processing the large volume of data (about 1,500 scenes or 1 TB per day) in a timely manner and generating geometrically accurate orthorectified products. After three-year research and development, a high performance system has been built and successfully delivered. The high performance system has a service oriented architecture and can be deployed to a cluster of computers that may be configured with high end computing power. The high performance is gained through, first, making image processing algorithms into parallel computing by using high performance graphic processing unit (GPU) cards and multiple cores from multiple CPUs, and, second, distributing processing tasks to a cluster of computing nodes. While achieving up to thirty (and even more) times faster in performance compared with the traditional practice, a particular methodology was developed to improve the geometric accuracy of images acquired from Chinese satellites (including HJ-1 A/B, ZY-1-02C, ZY-3, GF-1, etc.). The methodology consists of fully automatic collection of dense ground control points (GCP) from various resources and then application of those points to improve the photogrammetric model of the images. The delivered system is up running at CRESDA for pre-operational production and has been and is generating good return on investment by eliminating a great amount of manual labor and increasing more than ten times of data throughput daily with fewer operators. Future work, such as development of more performance-optimized algorithms, robust image matching methods and application workflows, is identified to improve the system in the coming years.

SPIE Journal Paper | 1 April 2003

Development of anisotropic diffusion to segment texture images

Huawu Deng, Jun Liu, Kap Chan

JEI, Vol. 12, Issue 02, (April 2003) https://doi.org/10.1117/12.10.1117/1.1557155

KEYWORDS: Image segmentation, Diffusion, Annealing, Anisotropic diffusion, Image processing, Feature extraction, Gaussian filters, Image fusion, Image filtering, MATLAB

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