HPC MSU
Name:James V. Aanstoos (Jim)
Associated Centers:Geosystems Research Institute
Position:Faculty
Email: aanstoos@gri.msstate.edu
Office:High Performance Computing Building,
Office Phone:(662) 325-8278
Address:Mailstop 9627
2 Research Blvd
Mississippi State, MS 39762

Biography:Dr. Aanstoos is an Associate Research Professor Emeritus of the Geosystems Research Institute (GRI) at Mississippi State University. He conducts research on remote sensing and radar analysis. Other recent research activities have focused on satellite rainfall estimation, small satellite engineering, multispectral land cover classification and accuracy assessment, and terrain database visualization. His past work has also included: development of a multispectral sensor simulator; visualization of wind shear models; development of software for data quality assurance.
EDUCATION:
B.S., Electrical and Computer Engineering, Rice University, 1977
M.E.E, Rice University, 1979
Ph.D., Atmospheric Science, Purdue University, 1996
EXPERIENCE:
Mississippi State University, Geosystems Research Institute
Associate Research Professor Emeritus, 2016-present
Mississippi State University, Geosystems Research Institute
Associate Research Professor, 2006-2016
Cary Academy, Cary, NC
Director of Information Services, 2001-2006
Research Triangle Institute, Research Triangle Park, NC
Senior Research Engineer, 1997-2001
Research Electrical Engineer, 1979-1997
North Carolina State University
Adjunct instructor, Electrical and Computer Engineering, 1980-1985

PROFESSIONAL MEMBERSHIPS:
IEEE (senior member); American Geophysical Union
PROFESSIONAL HONORS AND SERVICE:
Applied Imagery Pattern Recognition (AIPR) Executive Committee, Treasurer 1995-99, General Chair 2001-2002, AIPR 2000 Program Chair
Technical Advisory Committee to North Carolina statewide land cover mapping project, 1995


Research Interest: Image Processing, Remote Sensing, Scientific Visualization

Publications: Marapareddy, R., Aanstoos, J.V., & Younan, N. H. (2016). Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery. journal of sensors. www.mdpi.com. 16(6), 898. DOI:10.3390/s16060898. [Document] [Document Site]

Ramakalavathi, M., Aanstoos, J.V., & Younan, N. H. (2016). Supervised Classification Method with Efficient Filter Techniques to Detect Anomalies on Earthen Levees Using Synthetic Aperture Radar Imagery. European Space Agency Living Planet Symposium. Prague, Czech Republic, 9-13 May 2016. [Document Site]

Ramakalavathi, M., Aanstoos, J.V., & Younan, N. H. (2016). Supervised Classification Method with Efficient Filter Techniques to Detect Anomalies on Earthen Levees Using Synthetic Aperture Radar Imagery. European Space Agency Living Planet Symp.,. Prague, Czech Republic, 9-13 May 2016: lps16.esa.int. [Document Site]

Dabbiru, L., Wei, P., Harsh, A., Ball, J. E., Aanstoos, J.V., Doyle, J., Jackson, S., & Newman, J. (2015). Runway Assessment via Remote Sensing. 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). Cosmos Club, Washington, DC: IEEE. 1-5. [Abstract]

Marapareddy, R., Aanstoos, J.V., & Younan, N. H. (2015). Unsupervised Classification of SAR Imagery Using Polarimetric Decomposition to Preserve Scattering Characteristics. Applied Imagery Pattern Recognition (AIPR) -2015. Washington DC. [Document]

Total Publications by this Author: 52