Texas A&M University
O&M Building 707B
College Station, Texas 77843
- GEOG 203, Planet Earth: An Introduction to Earth System Science
- GEOG 361, Remote Sensing in Geosciences
- GEOG 390, Principles of Geographic Information Systems
- GEOG 475, Advanced Topics in GIS
- GEOG 651, Remote Sensing for Geographical Analysis
- GEOG 660, Applications in GIS
- GEOG 662, GIS in Land and Property Management
- GEOG 665, GIS-Based Spatial Analysis and Modeling
- GEOG 695, Frontiers in Geographic Information Science
Dr. Anthony M. Filippi
Ph.D. Geography, University of South Carolina, 2003
M.S. Geography, University of South Carolina
B.A. Geography, Kansas State University
Dr. Filippi is a remote-sensing and geographic information-processing (GIP) scientist with principal research interests in imaging spectroscopy, hyperspectral optical remote sensing of rivers and the coastal ocean, geographic information system (GIS)-based modeling and spatial analysis, and data fusion. His research combines remote sensing, aquatic (riverine and ocean) optics, GIScience, and machine learning, and his research agenda includes riverine/floodplain, coastal marine, and terrestrial optical systems.
One of Dr. Filippi’s current research interests focuses 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 images acquired over coastal and other waters. In addition to addressing problems in the littoral ocean, he conducts various inquiries in the coastal margins, including coastal wetland mapping, as well as in floodplain environments. Dr. Filippi also has continual involvement in terrestrial land-cover and vegetation investigations, including a variety of agricultural studies and hazardous/radiological waste site monitoring using airborne and satellite remote sensing. Dr. Filippi is a faculty member of the Fluvial-GEOS Lab, a research group focused on the use of remote-sensing and GIS to study riverine/floodplain research issues.
- forthcoming, Güneralp, I., Filippi, A.M., and Hales, B.*, River flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines. GIScience & Remote Sensing.
- Filippi, A.M., Bhaduri, B.L., Naughton, T., King, A.L., Scott, S.L., and Güneralp, I. 2012. Hyperspectral aquatic radiative transfer modeling using a high-performance cluster computing-based approach. GIScience & Remote Sensing, 49(2): 275-298. http://dx.doi.org/10.2747/1548-1603.49.2.275
- Filippi, A.M., Archibald, R., Bhaduri, B.L., and Bright, E.A. 2009. Hyperspectral agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE). Optics Express, 17(26): 23823-23842. doi:10.1364/OE.17.023823. http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-26-23823
- Filippi, A.M. and Kubota, T. 2009. Conditioning of reflectance signals by linear diffusion for improving narrow-band ratio-based remote-sensing bottom depth retrieval in shallow coastal waters. Journal of Applied Remote Sensing, 3, 033539, doi:10.1117/1.3211116.
- 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. In Introductory Digital Image Processing: A Remote Sensing Perspective. Third Edition, Upper Saddle River, NJ: Prentice Hall, pp. 431-465.