Carruth, D. W.,
Goodin, C.,
Dabbiru, L., Scherer, N.,
Moore, M.,
Hudson, C. R., Cagle, L. D., & Jayakumar, P. (2024). Comparing Real and Simulated Performance for an Off-Road Autonomous Ground Vehicle in Obstacle Avoidance.
Journal of Field Robotics. Wiley.
41(3), 798-810.
DOI:10.1002/rob.22289. [
Abstract] [
Document Site]
Dabbiru, L., Aanstoos, J.V.,
Ball, J. E., & Younan, N. H. (2017). Screening Mississippi River Levees Using Texture-based and Polarimetric-based Features from Synthetic Aperture Radar Data.
Electronics. MDPI.
1, 20. [
Abstract] [
Document Site]
Sehat, S.,
Vahedifard, F., Aanstoos, J.V.,
Dabbiru, L., & Hasan, K. (2014). Using In situ Soil Measurements for Analysis of a Polarimetric SAR-based Classification of Levee Slump Slides in the Lower Mississippi River.
Engineering Geology. Elsevier.
181, 157–168.
DOI:10.1016/j.enggeo.2014.07.007. [
Document Site]
Nobrega, R. A. A., Aanstoos, J.V., Gokaraju, B., Mahrooghy, M.,
Dabbiru, L., & O'Hara C. (2013). MAPPING WEAKNESSES IN THE MISSISSIPPI RIVER LEVEE SYSTEM USING MULTI-TEMPORAL UAVSAR DATA.
Brazilian Journal of Cartography, Photogrammetry and Remote Sensing. State University of Rio de Janeiro, Rio de Janeiro, Brazil: Brazilian Society for Cartography.
65(4), 681-694. [
Abstract]
Luo, Y., Feng, X., Houser, P., Anantharaj, V., Fan, X., Lannoy, G. D., Zhan, X., &
Dabbiru, L. (2013). Potential Soil Moisture Products from the Aquarius Radiometer and Scatterometer Using an Observing System Simulation Experiment.
Geoscientific Instrumentation, Methods and Data Systems. Copernicus Publications.
2, 113 - 120.
DOI:10.5194/gi-2-113-2013. [
Abstract] [
Document] [
Document Site]
Goodin, C., Sharma, S., Doude, M.,
Carruth, D. W.,
Dabbiru, L., &
Hudson, C. R. (2019). Training of Neural Networks with Automated Labeling of Simulated Sensor Data.
SAE Technical Paper 2019-01-0120. Detroit, MI.
DOI:10.4271/2019-01-0120. [
Abstract] [
Document Site]
Dabbiru, L., Aanstoos, J.V., &
Ball, J. E. (2017). Machine Learning Framework for Levee Slump Slide Detection Using Synthetic Aperture Radar Imagery.
SEDAAG 2017. Starkville, MS.
Dabbiru, L., Aanstoos, J.V., & Younan, N. H. (2016). Fusion of Hyperspectral and L-band SAR Data for Analyzing Impacts of Oil Spill Detection.
ESA Living Planet Symposium 2016. Prague, Czech Republic. [
Document]
Hasan, K., Aanstoos, J.V., Mahrooghy, M.,
Dabbiru, L., & Dunbar, J. B. (2011). Characterizing Mississippi River Levee Segments Using Soils and Geologic Data.
Proceeding 2011 Symposium on the Application of Geophysics to Environmental and Engineering Problems. Charleston, SC. [
Abstract] [
Document]
Aanstoos, J.V., O'Hara C., Prasad, S.,
Dabbiru, L., Nobrega, R. A. A., & Lee, M. (2009). Screening of Earthen Levees Using Synthetic Aperture Radar.
2009 Fall Meeting. San Francisco, CA, USA: American Geophysical Union. [
Abstract] [
Document]
Luo, Y., Houser, P. R., Anantharaj, V. G., Fan, X.,
Dabbiru, L., Turlapaty, A.C., Aanstoos, J.V., & DeLannoy, G. J. (2008). Potential L-Band Aquarius Radiometer and Scatterometer Soil Moisture Products from an Observing System Simulation Experiment.
2008 Fall Meeting. San Francisco, California, USA: American Geophysical Union. [
Abstract]
Dabbiru, L.,
Goodin, C.,
Carruth, D. W., Aspin, Z., Carrillo, J., & Kaniarz, J. (2024). Simulation Fidelity Analysis Using Deep Neural Networks.
Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II. National Harbor, MD, USA: SPIE.
13035.
DOI:10.1117/12.3012275. [
Abstract] [
Document Site]
Goodin, C.,
Carruth, D. W.,
Dabbiru, L., Aspin, Z., Carrillo, J. T., & Kaniarz, J. (2023). Fidelity Requirements for Simulating Sensor Performance in Autonomous Ground Vehicles.
Proc SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications. Orlando, FL, USA: SPIE.
12529.
DOI:10.1117/12.2661663. [
Abstract] [
Document Site]
Dabbiru, L.,
Goodin, C.,
Carruth, D. W., & Boone, J. (2023). Object Detection in Synthetic Aerial Imagery Using Deep Learning.
Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023. Orlando, FL, USA: SPIE.
12540.
DOI:10.1117/12.2662426. [
Abstract] [
Document Site]
Yu, J.,
Chen, J.,
Dabbiru, L., &
Goodin, C. (2023). Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance.
Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure. Orlando, FL.
DOI:10.1117/12.2663098. [
Document Site]
Goodin, C.,
Carruth, D. W.,
Dabbiru, L., Cagle, L. D., Monroe, J. G., & Parker, M. W. (2023). Generating Medium-scale Synthetic Snowy Scenes for Testing Autonomous Vehicle Navigation.
Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II. National Harbor, MD, USA: SPIE.
13035.
DOI:10.1117/12.3009866. [
Abstract] [
Document Site]
Goodin, C.,
Carruth, D. W.,
Dabbiru, L.,
Hudson, C. R., Cagle, L. D., Scherer, N.,
Moore, M., & Jayakumar, P. (2022). Simulation-based Testing of Autonomous Ground Vehicles.
Proc. SPIE 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022. Orlando, FL, USA: SPIE.
12115.
DOI:10.1117/12.2620502. [
Abstract] [
Document Site]
Meadows, W. S.,
Hudson, C. R.,
Goodin, C.,
Dabbiru, L., Powell, B., Doude, M.,
Carruth, D. W., Islam, M.,
Ball, J. E., & Tang, B. (2019). Multi-LiDAR Placement, Calibration, Co-registration, and Processing on a Subaru Forester for Off-road Autonomous Vehicles Operations.
Proceedings Volume 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019. Baltimore, MD.
DOI:10.1117/12.2518915. [
Abstract] [
Document Site]
Dabbiru, L., Aanstoos, J.V.,
Ball, J. E., & Younan, N. H. (2018). Machine Learning Framework for Mapping of Mississippi River Levees and Damage Assessment Using Terrasar-X Data.
IEEE Geoscience and Remote Sensing Society (IGARSS), July 2018. Velencia, Spain. [
Abstract]
Samiappan, S.,
Dabbiru, L., &
Moorhead, R. J. (2016). Fusion of Hyperspectral and LiDAR Data Using Random Feature Selection and Morphological Attribute Profiles.
WHISPERS. Los Angeles, CA: 8th Workshop on hyperspectral image and signal processing: Evolution in Remote Sensing. [
Abstract]
Dabbiru, L., Wei, P., Harsh, A., White, J.,
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]
Dabbiru, L.,
Samiappan, S., Nobrega, R. A. A., Aanstoos, J.V., Younan, N. H., &
Moorhead, R. J. (2015). Fusion of Synthetic Aperture Radar and Hyperspectral Imagery to Detect Impacts of Oil Spill in Gulf of Mexico.
IGARSS 2015 proceedings. Milan, Italy: IEEE.
Dabbiru, L., Aanstoos, J.V., & Younan, N. H. (2014). Comparison of L-Band and X-Band Polarimetric SAR Data Classification for Screening Earthen Levees.
IEEE Geoscience and Remote Sensing Society (IGARSS). Quebec, Canada: IEEE. [
Abstract] [
Document]
Sehat, S.,
Vahedifard, F., Aanstoos, J.V.,
Dabbiru, L., Hasan, K., & Mooney, M. (2014). Analysis of the Output from a Radar-based Levee Monitoring System Using In-situ Soil Data.
ASCE 2014 Geo-CongressProc., Geo-Congress 2014: Geo-Characterization and Modeling for Sustainability, Geotechnical Special Publication No. 234. Atlanta, GA: American Society of Civil Engineers. 943-952.
DOI:10.1061/9780784413272.092.
Dabbiru, L., Aanstoos, J.V., Hasan, K., Younan, N. H., & Li, W. (2013). Landslide Detection on Earthen Levees with X-band and L-band Radar Data.
2013 IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE.
DOI:10.1109/AIPR.2013.6749306. [
Abstract]
Aanstoos, J.V., Hasan, K., O'Hara C.,
Dabbiru, L., Mahrooghy, M., Nobrega, R. A. A., & Lee, M. (2012). Detection of Slump Slides on Earthen Levees Using Polarimetric SAR Imagery.
Proceedings of the 2012 IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE. [
Abstract] [
Document]
Dabbiru, L., Aanstoos, J.V., Mahrooghy, M., Li, W., Shanker, A., & Younan, N. H. (2012). Levee Anomaly Detection Using Polarmetric Synthetic Aperture Radar Data.
IGARSS 2012. Munich, Germany: IEEE. [
Abstract] [
Document]
Aanstoos, J.V., Hasan, K., O'Hara C., Prasad, S.,
Dabbiru, L., Mahrooghy, M., Gokaraju, B., Lee, M., & Nobrega, R. A. A. (2011). Earthen Levee Monitoring with Synthetic Aperture Radar.
Proc. IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE. [
Abstract] [
Document]
Dabbiru, L., & Aanstoos, J.V. (2010). Classification of Levees Using Polarimetric Synthetic Aperture Radar (SAR) Imagery.
Proc. 2010 39th IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE. [
Abstract] [
Document]
Aanstoos, J.V., Hasan, K., O'Hara C., Prasad, S.,
Dabbiru, L., Mahrooghy, M., Nobrega, R. A. A., Lee, M. L., & Shrestha, B. (2010). Use of Remote Sensing to Screen Earthen Levees.
Proc. 2010 39th IEEE Applied Imagery Pattern Recognition Workshop. Washington, DC: IEEE. [
Abstract] [
Document]
Nobrega, R. A. A., Aanstoos, J.V., &
Dabbiru, L. (2010). An Innovative Approach to Aid the Detection of Anomalies Along the Mississippi Levees by Bridging SAR and GIS.
2010 GEOSPATIAL CONFERENCE. Athens, GA: University of Georgia. [
Abstract] [
Document] [
Document Site]
Anderson, D., Johnson, E., Mita, D.,
Dabbiru, L., Katragadda, S., Lewis, D., & O'Hara C. (2007). Investigating Pathways from the Earth Science Knowledge Base to Candidate Solutions.
AGU Fall 2007 Conference. San Francisco, CA. [
Abstract]
Dabbiru, L., O'Hara C., Shaw, D. R., Kim, S., Aanstoos, J.V., Shrestha, B., Katragadda, S., Anderson, D., Policelli, F., & Keblawi, N. (2006). Designing and Developing a NASA Research Projects Knowledge Base and Implementing Knowledge Management and Discovery Techniques.
Sanfrancisco, CA. [
Document]
Dabbiru, L., Banerjee, M., & Bhattacharya, P. (2004). Design, Fabrication and Integration of a Multi Sensor Chip.
Proceedings of the National Conference on Integrating Practice into Engineering Education. Dearborn, MI. [
Abstract]
Dabbiru, L., Aanstoos, J.V., Younan, N. H., &
Moorhead, R. J. (2016). Levee Anomaly Detection Using Synthetic Aperture Radar Imagery.
Naval Oceanographic Office 4th Annual Operational Oceanography Science and Technology Poster Symposium. Stennis Space Center, MS. [
Abstract] [
Document]
Dabbiru, L.,
Samiappan, S., Aanstoos, J.V., &
Moorhead, R. J. (2016). Fusion of Synthetic Aperture Radar and Hyperspectral Imagery to Detect Impacts of Oil Spill in Gulf of Mexico.
Naval Oceanographic Office 4th Annual Operational Oceanography Science and Technology Poster Symposium. Stennis Space Center, MS. [
Abstract] [
Document]
Dabbiru, L., Aanstoos, J.V., Hasan, K., Mahrooghy, M., Nobrega, R. A. A., Gokaraju, B., & Younan, N. H. (2011). Characterizing Levees Using Polarimetric and Interferometric Synthetic Aperture Radar Imagery.
AGU Fall Meeting 2011. San Francisco, CA, USA. [
Abstract] [
Document]
Aanstoos, J.V., Hasan, K., Mahrooghy, M.,
Dabbiru, L., Nobrega, R. A. A., & Prasad, S. (2011). Screening of Earthen Levees Using TerraSAR-X Radar Imagery.
4th TerraSAR-X Science Team Meeting. Oberpfaffenhofen, Germany: DLR (German Aerospace Center). [
Abstract] [
Document]
O'Hara C., Katragadda, S., Anderson, D.,
Dabbiru, L., Kim, S., Mita, D., & Johnson, E. (2007). The NASA Earth Science Knowledge Base (ESKB).
AGU Fall 2007 Conference. San Francisco, CA. [
Abstract]
Dabbiru, L. (2015). Earthen Levee Slide Detection via Automated Analysis of Synthetic Aperture Radar Imagery. Mississippi State University: Mississippi State University. [
Abstract]