Products

For Mars Mineral products, image data were used:

Data taken by Mars rover, Nasa-JPL (www.jpl.nasa.gov)

 

Mars Surface Minerals: Spirit images, JPL (www.Jpl.nasa.gov)

 

Mars Surface Minerals: Spirit images, JPL (www.jpl.nasa.gov)

 

Advanced Simulation Products

Advanced simulation Product: Simulation of Mars Minerals

 

                   

Landsat TM scene #1 ID: Y5013916033x07        Landsat TM scene #1 ID: TM930810
  Advanced simulation Product: Corrected images produced through atmospheric correction

 

 

For our Nanoindentation products, see sample publications (4 and 5)

Selected Publications

1. Elmahboub, Widad. A combined methodology to produce highly accurate classification for AVIRIS hyperspectral data, Canadian Journal of Remote Sensing, 2009, 35(4), pp. 321-335.

2. Elmahboub, Widad, Scarpace, F., Smith, B. A highly Accurate Classification of TM Data through Correction of Atmospheric Effects. Remote Sensing, 2009, 1, 278-299.

3. Elmahboub, Widad. A simulated Linear Mixture Model to Improve the Classification Accuracy of Satellite Data, Journal of SCI, 2006, 3 (1), pp. 25-29.

4. A.A. Elmustafa, A.A. Ananda, W.M. Elmahboub. “Dislocation Mechanics Simulation of the Bilinear Behavior in Micro and Nanoindentation”, Journal of Materials Research, 2004, 19 (3), pp. 768-779.

5. A.A. Elmustafa, A.A. Ananda, and W.M. Elmahboub. “Bilinear Behavior in Nano and Microindentation Tests of fcc Polycrystalline Materials.” J. Eng. Mater. Tech., 2004, 126, pp. 353-359.

6. Ira Walker, V. Chakrapani, and Widad Elmahboub. “The Development of a Shape Factor Instability Index to Guide Thunderstorm Forecast for Aviation Safety”. Journal of Applied Meteorological Applications, 2008, 15 (4), pp. 465-473.  

7. Elmahboub, Widad. Integrating Atmospheric correction and neural network to accurately classify an urban area using ASTER data. Canadian Journal of Remote Sensing (special issue), 2010 (accepted).

8. Elmahboub Widad, Yankey Edawrd, Kerwin Olivia. Modeling and Simulation on Signatures of Mars Minerals. Virginia Academy of Science Journal (VAS), 2010 (accepted).

9. W.M. Elmahboub, F. Scarpace, B. Smith, 2006, “Algorithm and Mathematical Modeling for Atmospheric Correction and Classification Accuracy for Hyperspectral Data”, World Multiconference on Systemics, Cybernetics and Informatics (WMSCI) July 2006, V.III, pp. 56-60.
10. W.M. Elmahboub and E. Yankey, “Spectral Analysis for Mars Surface Minerals using Hubble Telescope Digital Data”, Signal and Image Processing (SIP) Honolulu, Hawaii, August 2005, pp. 420-422.

11. W.M. Elmahboub, “Degradation of Aerosols through Mathematical Modeling Algorithm to Improve the Utility of Remote Sensing Data”, WMSCI, Orlando, Florida, July, 2005.Vol. IX, pp. 192-195.

12. W.M. Elmahboub, F. Scarpace, B. Smith, 2004. “A simulated Linear Mixture Model to Improve Classification Accuracy Utilizing Degradation of atmospheric Effect”, WMSCI Conference Proceedings, July 2004, Vol XIII: pp 140-143.

13. W.M. Elmahboub, F. Scarpace, B. Smith, 2004, “Aerosol Degradation to Improve Interpretation of Surface Remote Sensing Data”, SPIE Conference Proceedings, Canary Island, Spain, July 2004, Abstract # 5574A-09.

14. W.M. Elmahboub, 2004, “Digital Data Processing and interpretation from mars Surface, SPIE Conference Proceedings, Canary Island, Spain, July 2004, Abstract # 5573-51.

15. W.M Elmahboub “Imaging H2O on Mars” NASA NIA Planetary Sciences Workshop, Hampton VA January 20-21, 2004.

16. W.M. Elmahboub, F. Scarpace, B. Smith,. “An Integrated Methodology to Improve Classification Accuracy of Remote Sensing Data”, 2003 IEEE International Geosciences and Remote Sensing Symposium Proceedings, Toulouse France, 2003, Vol. IV: pp 2161-2163.

16. W.M. Elmahboub, “Digital Data Processing and Interpretation: Earth, Mars”, Astrobiology Symposium, Hampton University, November 21, 2003

18. Zhi Zeng, et al. “Similarity-based image classification via kernelized sparse representation”, submitted to International Conference on Image Processing (ICIP’2010), 2010
19. Zhi Zeng, et al. “A hierarchical generative model for generic audio documentcategorization”, in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP’2010), pp. 2418-2421, 2010.
20. Zhi Zeng, et al. “A novel approach to musical genre classification using probabilistic latent semantic analysis model”, in Proceedings of the International Conference on Multimedia & Expo (ICME’2009), pp. 486-489, 2009.
21. Zhi Zeng, et al. “A novel video classification method based on hybrid generative/discriminative models”, Lecture Notes in Computer Science, Structural, Syntactic, and Statistical Pattern Recognition(S+SSPR’2008), pp. 705-713, 2008.
22. Zhi Zeng, et al. “Program segmentation in a TV video stream using acoustic cues”, in Proceedings of the International Conference on Audio, Language and Image Processing (ICALIP’2008), pp. 748-751, 2008.