Dr. Anthony M. Filippi

Research Interests

Dr. Filippi is a remote sensing and geographic information processing (GIP) scientist with principal research interests in imaging spectroscopy, hyperspectral optical remote sensing of the coastal ocean, geographic information system (GIS)-based modeling and spatial analysis, and data fusion (e.g., fusing radar and optical imagery). His research combines remote sensing, ocean and environmental optics, GIScience, and machine learning, and his research agenda includes coastal marine and terrestrial optical systems.

Dr. Filippi’s current research interests focus on the development of hyperspectral remote-sensing inversion algorithms to estimate water column properties (inherent optical properties (IOPs) and constituent concentrations), bathymetry, bottom optical properties (BOPs), and bottom type information from remote sensor imagery acquired over coastal waters. In addition to addressing problems in the littoral ocean, he conducts various inquiries in the coastal margins, including coastal wetland mapping. Dr. Filippi also has continual involvement in terrestrial vegetation investigations, including agricultural and hazardous/radiological waste site monitoring using airborne and satellite remote sensing. Dr. Filippi is an Advisory Council Member in the Sustainable Coastal Margins Program (SCMP), Texas A&M University, an interdisciplinary research group focusing on coastal problems.

Selected Publications

• Filippi, A.M., and Archibald, R. 2009. Support Vector Machine-Based Endmember Extraction. IEEE Transactions on Geoscience and Remote Sensing, 47(3): 771-791. doi:10.1109/TGRS.2008.2004708.
• Filippi, A. M., and Kubota, T. 2008. Introduction of spatial smoothness constraints via linear diffusion for optimization-based hyperspectral coastal ocean remote-sensing inversion. Journal of Geophysical Research, 113, C03013, doi:10.1029/2007JC004441.
• Filippi, A. M. 2007. Derivative-neural spectroscopy for hyperspectral bathymetric inversion. Professional Geographer, 59(2): 236-255.
• Filippi, A. M., and Jensen, J. R. 2007. Effect of continuum removal on hyperspectral coastal vegetation classification using a fuzzy learning vector quantizer. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1857-1869.
• Filippi, A. M., Carder, K. L., and Davis, C. O. 2006. Vicarious calibration of the PHILLS hyperspectral sensor using a coastal tree-shadow method. Geophysical Research Letters, 33, L22605, doi:10.1029/2006GL027073.
• Filippi, A. M., and Jensen, J. R. 2006. Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment, 100: 512-530.
• Jensen, J. R., and Filippi, A. M. 2005. Thematic Information Extraction: Hyperspectral Image Analysis. InIntroductory Digital Image Processing: A Remote Sensing Perspective. Third Edition, Upper Saddle River, NJ: Prentice Hall, pp. 431-465.

Links

• Sustainable Coastal Margins Program, Texas A&M University