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Hyperspectral Imaging and Sounding of the Environment

25 June 2019 – 27 June 2019 San Jose McEnery Convention Center, San Jose, California United States


  • Atmospheric measurements, modeling, and compensation for atmospheric effects
  • Coastal and ocean remote sensing and modeling
  • Phytoplankton functional group and species discrimination
  • Thermal hyperspectral imaging
  • Material identification for monitoring air and water quality
  • Soil studies, including soil classification, soil moisture content, and trafficability
  • Species discrimination and mapping of vegetation in forests and wetlands
  • Precision agriculture
  • Hyperspectral applications in the mining, oil, and gas industries
  • Hyperspectral imaging for urban planning and development
  • Weather prediction
  • Urban and rural pollution monitoring
  • Hyperspectral imaging for industrial quality analysis and process control
  • New spectrometer design, development, and sensor characterization
  • Planned and deployed operational systems
  • Innovative signal and digital image processing techniques including image segmentation, pattern recognition, neural networks, and machine learning approaches
  • Dimension reduction and information content analysis
  • Fusion with active or passive sensors and visualization algorithms
  • Snapshot/Video rate hyperspectral imaging
  • Spectral inversion techniques such as deconvolution, derivatives, optimal estimation, and spectral fingerprinting
  • Radiative transfer modeling



  • Liane Guild, NASA 
    Airborne Calibration and Validation Instrumentation for Current and Next Generation Satellite Ocean Color Observations
  • Xianglei Huang, University of MichiganUnited States 
    Challenges and Opportunities in the Far-IR Remote Sensing
  • Sanna Kaasalainen, FinnishGeospatial Research InstituteFinland 
    Multispectral Terrestrial LiDAR: Improving Active Spectral Sensing of Low Reflectance Targets
  • Yingying Ma, Wuhan University 
    Hyperspectral Imaging and Sounding of the Environment
  • Sherry Palacios, Bay Area Environmental Research Inst. 
    Thinking Like a Data Scientist: Phytoplankton Functional Type Algorithms and Hyperspectral Imagery
  • William Philpot, Cornell UniversityUnited States 
    The Soil Line: Moisture-independent Soil Reflectance Spectra
  • Alan Schaum, US Naval Research LaboratoryUnited States 
    The Eightfold Paths to Clairvoyant Fusion
  • Sebastian Schmidt, Univ of Colorado/LASPUnited States 
    Impact of Broken Clouds on Trace Gas Spectroscopy From Low Earth Orbit
  • Robert Sundberg, Spectral Sciences Inc.United States 
    The Impact of Partly Cloudy Skies on Remote Sensing Data Products
  • James Theiler, Los Alamos National LaboratoryUnited States 
    Background estimation in multispectral imagery
  • David Thompson, Jet Propulsion Laboratory, CaltechUnited States 
    Bayesian methods for remote coastal measurement using imaging spectroscopy
  • Jun Wang, University of Iowa 
    Using AIRS Hyperspectral Observations To Optimize Dust Refractive Index in Infrared Spectrum



Ka Lok Chan, German Aerospace Center (DLR), GermanyChair       

Wesley Moses, Naval Research Laboratory, USAChair   

Emmett Lentilucci, Rochester Institute of Technology, USA

Peter Pilewskie, University of Colorado at Boulder, USA

Saurabh Prasad, University of Houston, USA

Jacopo Taddeucci, Istituto Nazionale di Geofisica e Vulcan, Italy

Ping Yang, Texas A&M University, USA

Michael Yetzbacher, US Naval Research Laboratory, USA


Plenary Session

Melba Crawford

Purdue University, USA

Multi-modality Remote Sensing Data Acquisition and Analysis for High Throughput Phenotyping

Sensing technologies ranging from RGB cameras to hyperspectral imaging and LiDAR are rapidly gaining popularity for field-based high throughput phenotyping applications on airborne and ground-based platforms.  In addition to direct measurements of traditional phenotypes such as height, these sensors potentially provide surrogate measurements for plant structural characteristics (e.g. leaf count and leaf area index) and chemistry (e.g. photosynthesis, and plant stress). Opportunities and challenges associated with acquisition, processing, and analysis of high resolution RGB, VNIR/SWIR hyperspectral data, and discrete return LiDAR data acquired from UAVs for plant breeding experiments focused on advancing sorghum varieties for biofuels will be outlined.  Results from multi-modality, multi-temporal predictive modeling of complex phenotypes such as biomass using data driven machine learning and biophysical models will also be presented in the context of feature extraction and learning with limited training data.  Opportunities to exploit transfer learning across scales will also be discussed.

About the Speaker

Dr. Melba Crawford holds the Chair of Excellence in Earth Observation at Purdue University, where she is the Associate Dean of Engineering for Research and a professor in the Schools of Civil Engineering and Electrical and Computer Engineering, and the Department of Agronomy.  Her research interests focus on development of methods for signal and image processing, and applications of these algorithms to remote sensing problems in defense, agriculture, and natural resource management.  She is currently co-leading a joint initiative between the Purdue colleges of agriculture and engineering in development of advanced sensing technologies and analysis methodology for wheeled and UAV platforms, focused on high throughput phenotyping for plant breeding.

Dr. Crawford is a Fellow of the IEEE, Past President of the IEEE Geoscience and Remote Sensing Society, an IEEE GRSS Distinguished Lecturer, and the current Treasurer of the IEEE Technical Activities Board. She was a member of the NASA EO-1 Science Validation team and served on the NASA Earth System Science and Applications Advisory Committee and the advisory committee to the NASA Socioeconomic Applications and Data Center (SEDAC).

Alex Gaeta

Columbia University

Chip-Based Comb Spectroscopy

The ability to generate optical frequency combs in microresonators at milliwatt power levels offers the promise for high-precision spectroscopic instruments in highly robust, compact, and portable platforms.

About the Speaker

Alex Gaeta received his Ph.D. in 1991 in Optics from the University of Rochester.  He joined the faculty in the Department of Applied Physics and Applied Mathematics at Columbia University in 2015, where he is the David M. Rickey Professor.  Prior to  this, he was a professor in the School of Applied and Engineering Physics at Cornell University for 23 years.  He has published more than 230 papers in quantum and nonlinear optics. He co-founded PicoLuz, Inc. and has served as the founding Editor-in-Chief of Optica since 2014.  He is a Fellow of the OSA, APS, and IEEE, and was awarded the 2019 Charles H. Townes Medal from the OSA.

Peter Russo

Analog Photonics

High Performance Optical Phased Array LiDAR

Integrated optical phased arrays provide an attractive solution to LiDAR sensors by enabling solid-state, small-form-factor systems fabricated on 300mm wafers. We present recent results including high-performance beam steering and long-range LiDAR up to almost 200m."

About the Speaker

Peter Russo is Director of LiDAR at Analog Photonics. He received his Bachelor of Science in Electrical Engineering from University of Maryland, College Park in 2008. After graduating, he joined BAE Systems as part of the Engineering Leadership Development Program, through which he also received his Master of Science in Electrical Engineering from University of New Hampshire. At BAE Systems, he served as principle investigator on several active electro-optical systems programs. In 2015, he joined Formlabs, a 3D-printing startup, as a member of the electro-optical team. In 2017, Mr. Russo joined Analog Photonics as the LiDAR Architect to develop and commercialize silicon-photonic, optical phased array LiDAR for use on autonomous vehicles in both the automotive and DoD markets.


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