1. Usando ENVI para
extraer elementos
importantes desde
imágenes satelitales y
datos LiDAR
Cherie Muleh
Cherie.Muleh@exelisvis.com
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2. Extracting Building Features from LiDAR + Optical Data
Agenda
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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.