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Bormin Huang

Dr. Bormin  Huang

Research Scientist
University of Wisconsin-Madison

Space Science and Engineering Center
1225 W. Dayton St.
Madison WI 47905-6578
United States

tel: 608 265 2231
E-mail: bormin@ssec.wisc.edu

Area of Expertise

Satellite data compression, high-performance computing in remote sensing, remote sensing image processing, remote sensing forwward modeling and inverse problems


Dr. Bormin Huang has been serving as a Chair of SPIE Conference on Satellite Data Compression, Communications, and Processing since 2005, and a Chair of SPIE Europe Conference on High-Performance Computing in Remote Sensing since 2011. He is an Associate Editor of the Journal of Applied Remote Sensing (JARS), and serves, or has served, as the Guest Editor of JARS Special Section on "Satellite Data Compression", the Guest Editor of JARS Special Section on "High-Performance Computing in Applied Remote Sensing", and a Guest Editor of Special Issue on "Advances in Compression of Optical Image Data from Space" for the Journal of Electrical and Computer Engineering. He received his MSE in aerospace engineering from the University of Michigan, Ann Arbor, and his PhD in the area of satellite remote sensing from the University of Wisconsin-Madison. He is currently a research scientist and principal investigator at the Space Science and Engineering Center, the University of Wisconsin-Madison, where he advises and supports both national and international MS and PhD students and visiting scientists. He has authored and coauthored over 100 scientific and technical publications, including the Springer book "Satellite Data Compression". He has broad interest and experience in remote sensing science and technology, including satellite data compression, high-performance computing in remote sensing, remote sensing image processing, and remote sensing forward modeling and inverse problems.

Lecture Title(s)

Ultraspectral Sounder and Hyperspectral Imager Data Compression
With the advances in modern active and passive sensor technologies with higher spectral and/or spatial resolutions and faster scanning speeds, more powerful airborne and spaceborne instruments are being developed for remote sensing of the atmosphere, oceans, lands of Earth, and other planets. These technologies result in a significant increase in data volume. The increase presents a challenge to satellites with limited access to a growing congested radio frequency spectrum. Data compression techniques provide data reduction for effective downlink and rebroadcast as well as economic archiving. Data communication techniques facilitate robust information transfer over a limited-bandwidth noisy channel. This presentation will focus on the recent advances in hyperspectral imager and ultraspectral sounder data compression.

High-Performance Computing in Remote Sensing
Advances in sensor technology with higher spatial, spectral and temporal resolutions are revolutionizing the way remote sensing data are collected, managed and processed. Latest-generation instruments for Earth and planetary observation are now producing a nearly-continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly introduced new processing challenges. In particular, many current and future applications of remote sensing in Earth and space sciences require the incorporation of high performance computing techniques and practices to address applications with high societal impact such as retrieval of Earth and planetary atmospheres, monitoring of natural disasters including earthquakes and floods, or tracking of man-induced hazards such as wild-land and forest fires, oil spills and other types of chemical contamination. Many of these applications require timely responses for swift decisions which depend upon (near) real-time performance of algorithm analysis. These systems and applications can greatly benefit from high performance computing techniques and practices to speed up data processing, either after the data has been collected and transmitted to a ground station, or during the data collection procedure onboard the sensor in real-time fashion. Parallel and distributed computing facilities and algorithms as well as high-performance FPGA and DSP systems have become indispensable tools to tackle the issues of processing massive remote sensing data. In recent years, GPUs have evolved into highly parallel many-core processors with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the CPUs. A cost-effective GPU computer has become an affordable alternative to an expensive CPU computer cluster for many researchers performing various scientific and engineering applications.

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