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Usando ENVI para extraer elementos importantes desde imágenes satelitales y datos LiDAR-Cherie Muleh, Exelis, EE.UU.
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Usando ENVI para extraer elementos importantes desde imágenes satelitales y datos LiDAR-Cherie Muleh, Exelis, EE.UU.


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  • 1. Usando ENVI para extraer elementos importantes desde imágenes satelitales y datos LiDAR Cherie Muleh The information contained in this document pertains to software products and services that are subject to the controls of the Export Administration Regulations (EAR). The recipient is responsible for ensuring compliance to all applicable U.S. Export Control laws and regulations.
  • 2. Extracting Building Features from LiDAR + Optical Data Agenda > > > > Consideration of Data Availability and Usage Feature Extraction Methods Applying Methods to Extract Building Features Future Prospects for Building Feature Extraction
  • 3. An Abundance of Geospatial Data from which to Extract Features and Information Data Types > Color/IR Orthophotos > Multi/Hyperspectral > LiDAR > SAR Platforms > Aerial > Spaceborne > Terrestrial Prospects for future data > Commercial UAVs
  • 4. Valuing Remotely Sensed Data as a Source for Features Imagery is not just a base map, but a source of rich information that geospatial analysts can use to solve complex problems. > Provide data availability over broad and inaccessible areas > Improve timeliness of data acquisition > Potentially greater accuracy > Automated feature extraction for reduction in manual digitization > Advanced geospatial analysis using spectral image properties
  • 5. Extracting Information from Remotely Sensed Data Limitations or Opportunities, Given the Data Type Features of Interest > Vehicles > Transportation Networks > Structures > Natural Features > Human Activity
  • 6. Extracting Information from Remotely Sensed Data Needs for Feature Extraction > Increased availability of high- resolution images > Manual digitization > Semi-automated solution is highly desired Applications > Defense and Security > Transportation > Urban planning and mapping
  • 7. Object-Based Image Analysis What is an object? • An object is a region of interest with spatial, spectral (brightness and color), and/or texture characteristics that define the region • Pixels are grouped into objects, instead of single pixel analysis • May provide increased accuracy and detail for classification purposes
  • 8. Building Extraction Methods using Geospatial Data Pixel by Pixel Image 1.0 Water Pixels 0.5 56 4 3 2 1 Reflectance 0.0 1.0 Veg 0.5 0.0 1.0 Soil 0.5 Group materials based on their reflectance response per pixel 0.0 1 2 3 4 Band 5 6 > (+) Good for large area-based FX with low-med resolution data > (-) Poor edge detection without good spectral/spatial resolution; challenging for building extraction
  • 9. Building Extraction Methods using Geospatial Data Object-based Image Analysis Image Pixels Segmented Objects Merged Segmented Objects Complex Building Features > Computer vision technique involving image segmentation > Objects are classified into feature classes based contextual attributes: spatial, textural and spectral > Yields accurate building extraction; results and can be model-based
  • 10. Building Feature Extraction: An Important Aspect for Understanding an Operational Landscape For Planning and Risk Identification > Land use planning > Zoning, taxation > Structure inventory > Material Identification For Post-event Response > Disaster assessment > Response planning > Reconstruction monitoring Buildings are key foundational data layers for GIS and critical to decision analytics
  • 11. Building Extraction Methods using Geospatial Data Extracting Features from LiDAR Point Clouds Feature identification: 3D point cloud visualization > Manual process, but familiar and expedient Features interpreted from derivative raster products > Multi-step process > Feature delineations from interpolated height values > Use results with object-based FX DSM Features extracted from Point Clouds > Requires thicker point clouds > Based on 3D morphological filters > Proprietary or custom algorithms DEM Height Model
  • 12. Applying Methods to Extract Building Features Combining Optical and LiDAR Data for Decision Support Objective: > Efficiently extract building footprints > Use imagery to glean information about the structures that will provide situational awareness Process: > 3D Feature Extraction from hi-res LiDAR to capture building footprints > Conduct image processing routines using buildings as regions of interest Combine the best of what LiDAR and image processing have to offer
  • 13. Applying Methods to Extract Building Features: LiDAR Use Advanced 3D Algorithms to Process LiDAR Data
  • 14. Applying Methods to Extract Building Features: LiDAR 3D LiDAR Extraction Vector and Raster Products Classified Point Cloud Trees > Location, Elevation, Height, Radius Buildings > Location, Perimeter Vectors, Roof Face Vectors Power Lines > Power Line Vectors, Power Pole List, Power Line Attachment Points Terrain > Digital Surface Model (Grid and TIN), Digital Elevation Model, Ground contours Valuable GIS Data Layers
  • 15. Applying Methods to Extract Building Features: LiDAR Leverage Building Footprints and Elevation Products Determine Height Model > Raster data for additional processing/awareness of objects in the area Building Vectors > Immediate asset inventory > AOIs for additional processing DSM DEM Height Model
  • 16. Applying Methods to Extract Building Features: Optical Image Analysis Methods Using LiDAR-derived Products Topographic Modeling > Use raster height model data to determine roof slope & aspect on buildings Spectral Analysis > Apply object-based FX to multi/hyperspectral imagery, using building footprint ROIs > Capture additional spectral, textural, spatial attributes for additional analysis opportunities Height Model Spectral Image ROI ROI Roof Angle and Slope Roof Composition
  • 17. Future Perspective: Building Feature Extraction Better Data, Better Tools, Better Analysis Results… Improved Point Cloud FX > Denser data > MSI/HSI Spectral attribution > Improved algorithms Improved Object-Based FX > Better quality imagery > Better OBIA models 3D Visualizations & Modeling > Photorealism & accuracy > New 3D analysis methods Convergence of tools and methods will improve building FX, regardless of data type October 23, 2013 17
  • 18. Thank You © 2013 Exelis Visual Information Solutions, Inc.