University of Wisconsin GIFTS MURI University of Hawaii Contributions Paul G. Lucey Co-Investigator.

Slides:



Advertisements
Similar presentations
 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.
Advertisements

A Graphical Operator Framework for Signature Detection in Hyperspectral Imagery David Messinger, Ph.D. Digital Imaging and Remote Sensing Laboratory Chester.
Radiometric Corrections
Landsat-based thermal change of Nisyros Island (volcanic)
A U R A Satellite Mission T E S
REMOTE SENSING Presented by: Anniken Lydon. What is Remote Sensing? Remote sensing refers to different methods used for the collection of information.
Hyperspectral image processing and analysis
NRL09/21/2004_Davis.1 GOES-R HES-CW Atmospheric Correction Curtiss O. Davis Code 7203 Naval Research Laboratory Washington, DC 20375
Review of Remote Sensing Fundaments IV Infrared at High Spectral Resolution – Basic Principal & Limitations Allen Huang Cooperative Institute for Meteorological.
Remote sensing in meteorology
Hyperspectral Imagery
Lecture 13: Spectral Mixture Analysis Tuesday 16 February 2010 Last lecture: framework for viewing image processing and details about some standard algorithms.
ESS st half topics covered in class, reading, and labs Images and maps - (x,y,z,,t) Temporal data - Time-lapse movies Spatial data - Photos and.
Energy interactions in the atmosphere
Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005.
Mapping Roads and other Urban Materials using Hyperspectral Data Dar Roberts, Meg Gardner, Becky Powell, Phil Dennison, Val Noronha.
Digital Imaging and Remote Sensing Laboratory Real-World Stepwise Spectral Unmixing Daniel Newland Dr. John Schott Digital Imaging and Remote Sensing Laboratory.
Cirrus Cloud Boundaries from the Moisture Profile Q-6: HS Sounder Constituent Profiling Capabilities W. Smith 1,2, B. Pierce 3, and Z. Chen 2 1 University.
Sergey Mekhontsev National Institute of Standards and Technology Optical Technology Division, Gaithersburg, MD Infrared Spectral Radiance Scale.
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing ► Collects image data in many narrow contiguous spectral bands through the.
TAILING MODELLED AND MEASURED SPECTRUM FOR MINE TAILING MAPPING IN TUNISIAN SEMI-ARID CONTEXT N. Mezned 1,2, S. Abdeljaouad 1, M. R. Boussema
Remote Sensing for Mineral Exploration
Noise-Robust Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Data Gabriel Martín, Maciel Zortea and Antonio Plaza Hyperspectral.
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
Liane Guild, Brad Lobitz, Randy Berthold, Jeremy Kerr Biospheric Science Branch, NASA Ames Research Center, CA Roy Armstrong, James Goodman University.
Europlanet Strategic Workshop; General Assembly ESA ESTEC 26,27/02/ European data bank of spectral properties of minerals and their mixtures Maria.
Spectral Characteristics
Karnieli: Introduction to Remote Sensing
High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.
刘瑶.  Introduction  Method  Experiment results  Summary & future work.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
Jonatan Gefen 28/11/2012. Outline Introduction to classification Whole Pixel Subpixel Classification Linear Unmixing Matched Filtering (partial unmixing)
What is an image? What is an image and which image bands are “best” for visual interpretation?
Hank Revercomb, David C. Tobin, Robert O. Knuteson, Fred A. Best, Daniel D. LaPorte, Steven Dutcher, Scott D. Ellington, Mark W.Werner, Ralph G. Dedecker,
Digital Imaging and Remote Sensing Laboratory Spectral Signatures.
BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.
Digital Imaging and Remote Sensing Laboratory R.I.TR.I.TR.I.TR.I.T R.I.TR.I.TR.I.TR.I.T RIT ONR MURI Algorithm Development David Messinger LM LASS Status.
Summary of Spectroscopy Results. Recap  VNIR - visible/near-infrared spectrometer  µm wavelengths, reflectivity  Rover-mounted  Sensitive.
Validation of MODIS Snow Mapping Algorithm Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20 Thursday, 12 March.
LANL Hyperspectral Image Processing
Hyperspectral remote sensing
NASA’s Coastal and Ocean Airborne Science Testbed (COAST) L. Guild 1 *, J. Dungan 1, M. Edwards 1, P. Russell 1, S. Hooker 2, J. Myers 3, J. Morrow 4,
Hyperspectral Remote Sensing Ruiliang Pu Center for Assessment and Monitoring of Forest and Environmental Resources Department of Environmental Science,
Review of Spectral Unmixing for Hyperspectral Imagery Lidan Miao Sept. 29, 2005.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
1 GOES-R AWG Product Validation Tool Development Snow Cover Team Thomas Painter UCAR Andrew Rost, Kelley Eicher, Chris Bovitz NOHRSC.
Comparative Analysis of Spectral Unmixing Algorithms Lidan Miao Nov. 10, 2005.
May 15, 2002MURI Hyperspectral Workshop1 Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate.
NOTE, THIS PPT LARGELY SWIPED FROM
1 UW MURI Physical Modeling For Processing Hyperspectral Data – Non-UW Co-Is’ Progress Allen Huang, PI Co-Is: Paul L. HIGP, Univ. of Hawaii Ping Y., Univ.
Lunar Mineralogy Kaylan Meinecke ASU NASA Space Grant Lunar Reconnaissance Orbiter Office Mentor: Samuel Lawrence 18 April 2009.
© Crown copyright Met Office OBR conference 2012 Stephan Havemann, Jean-Claude Thelen, Anthony J. Baran, Jonathan P. Taylor The Havemann-Taylor Fast Radiative.
Remote Sensing Section Global soil monitoring and mineral mapping from emerging remote sensing technologies Sabine Chabrillat and the group “hyperspectral.
Surface Characterization 4th Annual Workshop on Hyperspectral Meteorological Science of UW MURI And Beyond Donovan Steutel Paul G. Lucey University of.
Selected Hyperspectral Mapping Method
Hyperspectral Sensing – Imaging Spectroscopy
Preprocessing for Hyperspectral Analysis
Hyperspectral Analysis Techniques
Hyperspectral Remote Sensing
Landsat-based thermal change of Nisyros Island (volcanic)
Hyperspectral Image preprocessing
Radiation in the Atmosphere
AIRS/GEO Infrared Intercalibration
By Narayan Adhikari Charles Woodman
Igor Appel Alexander Kokhanovsky
Lecture 20 – review Thursday, 11 March 2010 Labs: questions
Remote sensing in meteorology
Hyperspectral Remote Sensing
Presentation transcript:

University of Wisconsin GIFTS MURI University of Hawaii Contributions Paul G. Lucey Co-Investigator

Hawaii Capabilities Contribute To All Aspects of Wisconsin MURI Project

Overview— Surface Materials Properties Airborne hyperspectral data reduced to emissivity Laboratory data collection of materials spectral properties Directed airborne hyperspectral data collection Specialized surface materials properties of interest to GIFTS MURI Data collections in support of atmospheric model validation Hyperspectral analysis methodologies Target detection and surface materials classification algorithms.

AHI Airborne LWIR Hyperspectral Imager UV, VISNIR, MWIR/LWIR Field Spectrometers UH Operates Airborne, Field and Laboratory Hyperspectral Data Collection Systems for Collection of Surface Materials Spectral Properties Nicolet MWIR/LWIR Laboratory FTIR

Extensive Airborne Data Sets of Various Terrains Mosaic of (AHI) LWIR Data of Silver Lake CA End-Members Determined by N-FINDR with Constrained Unmix Abundance of Kaolinite calculated from spectral unmixing Kaolinite Quartzite Carbonates Mixed Clays and Other Silicates Mixed Clays LWIR spectra of mineral components in the scene

Airborne Hyperspectral and Field Ground Truth Solid: AHI Airborne hyperspectral Symbols: Field spectrometer ground truth

Field Hyperspectral Ground Truth and Laboratory Support

Directed AHI airborne hyperspectral data collection Specialized surface materials properties of interest to GIFTS MURI Data collections in support of atmospheric model validation

Gas detection using a matched filter with AHI Red area shows gas detection Single band TIR imageMatched filter detection Gas Absorption Peaks Probable reflected ozone Spectra from AHI Data Cube

Relevant Spectral Resolution AHI maximum spectral resolution (3 wavenumbers) is relevant to some GIFTS data collection modes Band Number Radiance

Hyperspectral analysis methodologies Target detection and surface materials classification algorithms Autonomous End-members and Spectral Unmixing 3 3 Spectral Endmembers Found Using Autonomous Method: N-Findr Spectra then “Unmixed” Using Linear Algebra Endmember spectra interpreted using spectrum library search

KaoliniteAlunite Buddingtonite Comparison of N-FINDR Endmember Spectra to Library Spectra N-FINDR is red Spectra Compare Best for Materials that have a near Pure Pixel in the Scene

Surface materials mapping Red: Silicates Halloysite Kaolinite Jarosite Buddingtonite Green: Carbonates Calcite Blue: Sulfates Alunite

MURI Progress— Continuing compilation of non-classified airborne hyperspectral data sets in units of surface emissivity in accessible on-line form Continued collection of laboratory data in support of surface emissivity library Test data collection of airborne hyperspectral data in Hawaii for atmospheric model validation.

Compilation of non-classified airborne hyperspectral data UH has collected 100’s of Gbytes of airborne hyperspectral data, but accessibility is poor (off-line DVD chronological storage) Data catalog is under development including geographic location, thumbnails and materials summaries.

Progress— Continued collection of laboratory data in support of surface emissivity library

Progress— Test data collection of airborne hyperspectral data in Hawaii for atmospheric model validation. AHI Flight Lines

Single band IR image of active flow field

Wavelength (microns) Emissivity Wavelength (microns) Emissivity SO 2 2 Metastable silica glass

along track Spatial spectral slice showing SO2 and metastable glass Brightness temperature Apparent Emissivity

UVS Ground truth measurements of SO2 abundance

Plan for Next Reporting Period Completion of on-line data base of laboratory spectra with simple data ingestion On-line data base/catalog of AHI airborne data Continue collection of directed validation airborne data runs Focus on aerosols and water vapor Using HIGP ground truthing tools Aerosol photometer NIR/SWIR spectral water vapor measurements GPS water vapor