Proposed Experiments:

Slides:



Advertisements
Similar presentations
Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Advertisements

Remote sensing, promising tool of the future Mária Szomolányi Ritvayné – Gabriella Frombach VITUKI CONSULT MOKKA Conference, June
Hyperspectral Image Acquisition and Analysis PECORA 15 Workshop 7 Airborne Remote Sensing: A Fast-track Approach to NEPA Streamlining for Transportation.
A Graphical Operator Framework for Signature Detection in Hyperspectral Imagery David Messinger, Ph.D. Digital Imaging and Remote Sensing Laboratory Chester.
Some Basic Concepts of Remote Sensing
A Fully Automated Approach to Classifying Urban Land Use and Cover from LiDAR, Multi-spectral Imagery, and Ancillary Data Jason Parent Qian Lei University.
Imagery for Forest R&D General requirements –Sub-crown spatial resolution –Fine spectral resolution (~10nm FWHM) –Season –Image collection coincident with.
Hyperspectral Imagery
Remote Sensing Forest Fires: Before and After Rob Gaboy & Aimee Treutlein.
Remote Sensing What can we do with it?. The early years.
Remote sensing is up! Inventory & monitoring Inventory – To describe the current status of forest Landcover / landuse classification Forest structure /
Mapping Roads and other Urban Materials using Hyperspectral Data Dar Roberts, Meg Gardner, Becky Powell, Phil Dennison, Val Noronha.
Aerial photography and satellite imagery as data input GEOG 4103, Feb 20th Adina Racoviteanu.
Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing ► Collects image data in many narrow contiguous spectral bands through the.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Liane Guild, Brad Lobitz, Randy Berthold, Jeremy Kerr Biospheric Science Branch, NASA Ames Research Center, CA Roy Armstrong, James Goodman University.
FLEX-US 2013 Airborne Campaign
1 Remote Sensing and Image Processing: 7 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: (x24290)
ARSF Data Processing Consequences of the Airborne Processing Library Mark Warren Plymouth Marine Laboratory, Plymouth, UK RSPSoc 2012 – Greenwich, London.
Using spectral data to discriminate land cover types.
Dr. Garver GEO 420 Sensors. So far we have discussed the nature and properties of electromagnetic radiation Sensors - gather and process information detect.
The role of remote sensing in Climate Change Mitigation and Adaptation.
University of Wisconsin GIFTS MURI University of Hawaii Contributions Paul G. Lucey Co-Investigator.
Remote Sensing and Image Processing: 7 Dr. Hassan J. Eghbali.
Geometric Enhancement to Physics-based Target Detection Mike Foster 15 Aug 06.
Share 2012 David Messinger June 6, DIRS Previous Experiment /Dissemination Efforts MegaCollect 2004 – Vis-NIR-SWIR HSI (2) – LWIR HSI – high resolution.
RASTERTIN. What is LiDAR? LiDAR = Light Detection And Ranging Active form of remote sensing measuring distance to target surfaces using narrow beams of.
Use of 3D Imaging for Information Product Development
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
Validation of MODIS Snow Mapping Algorithm Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota.
USGS - California Fire Response -Hyperspectral Remote Sensing
Beyond Spectral and Spatial data: Exploring other domains of information: 4 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Feng Li and Steve Lach Advanced Digital Image Processing.
Hyperspectral Remote Sensing Ruiliang Pu Center for Assessment and Monitoring of Forest and Environmental Resources Department of Environmental Science,
April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.
Beyond Spectral and Spatial data: Exploring other domains of information: 3 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Sub pixelclassification
Comparative Analysis of Spectral Unmixing Algorithms Lidan Miao Nov. 10, 2005.
Global Vegetation Monitoring Unit Problems encountered using Along Track Scanning Radiometer data for continental mapping over South America Requirement.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Surface Characterization 4th Annual Workshop on Hyperspectral Meteorological Science of UW MURI And Beyond Donovan Steutel Paul G. Lucey University of.
Counting the trees in the forest
Lab 2-4 Surveying Bear Lake Shorelines
Sensors Dr. Garver GEO 420.
Mapping Vegetation with Synthetic Aperture Radar:
The SHARE 2012 Data Collection
The SHARE 2012 Data Collection
Hyperspectral Sensing – Imaging Spectroscopy
LAND COVER CLASSIFICATION WITH THE IMPACT TOOL
Key Inputs / Requirements
PROBA scenes acquired over our study sites
SHARE 2012 Planning Meeting 9/6/12
Basic Concepts of Remote Sensing
HIERARCHICAL CLASSIFICATION OF DIFFERENT CROPS USING
LiDAR and Habitat Identification
Hyperspectral Remote Sensing
Polarstern Helicopters
WP300 – Recommendations for S2 and S3
Need for TEMPO-ABI Synergy
Why LiDAR makes hyperspectral imagery more valuable for forest species mapping OLI 2018 Andrew Brenner, Scott Nowicki & Zack Raymer.
ESS st half topics covered in class, reading, and labs
Hyperspectral Image preprocessing
By: Paul A. Pellissier, Scott V. Ollinger, Lucie C. Lepine
Resolution.
Isle of Wight Centre for the Coastal Environment
Field Photos
Remote sensing in meteorology
Hyperspectral Remote Sensing
Advances in Microclimate Ecology Arising from Remote Sensing
Presentation transcript:

Proposed Experiments: Multimodal Target/Change Detection (Blind) (Kerekes) Landcover Classification (Kerekes) Unmixing (Quantitative) (Kerekes) Complex LIDAR Surface (Kerekes) Geometric Control Range (need lead, Don Light/Don M.) H.S.I. with LIDAR Candice/Hemlock Forest (Jan) Sub-pixel Targets on Spectral Graph Creation/Analysis (Messinger) Sub-pixel Abundance Estimation from Spectral Unmixing (Messinger) 3D reconstruction (need PI) Landsat/LDCM OLI model/check out, TIRS water thermal (Schott/Gerace) MITRE Confusing Targets (Ariel) Shadow/Illumination(Emmett) SAR TBD (Gartley)

Summary of Initial PI requirements Target Many spectral targets to be constructed, forest, submerged water target, 3d target Geographic ROI or Type Canadice/Hemlock, Avon,Parking lots, grass fields, clutter, Geo. Control Range, Trees, Manmade Spectral Modality / Resolution H.S.I. <10nm Spatial Resolution WASP <30cm, WASP high overlap for 3D reconstruction H.S.I. <1m Specific Sensor WASP, 2 H.S.I. SpecTIR AVIRIS, NEON Airborne LIDAR YES 5pts/m2 Ground Based LIDAR YES structures TBD Atmospheric Meas. Visibility estimate, sky photos, instrumentation TBD Ground Based Meas. (concurrent?) YES - need to ID concurrent requirements, GPS Season Any or Summer Time of Day Solar Noon Repeat Coverage/Moving YES 1 request separated by 3-4 hours Summary of Initial PI requirements