Usando ENVI para extraer elementos importantes desde

imágenes satelitales y datos LiDAR

Cherie Muleh

Cherie.Muleh@exelisvis.com

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Agenda

> Consideration of Data Availability and Usage

> Feature Extraction Methods

> Applying Methods to Extract Building Features

> Future Prospects for Building Feature Extraction

Extracting Building Features from LiDAR + Optical Data

Data Types

> Color/IR Orthophotos

> Multi/Hyperspectral

> LiDAR

> SAR Platforms

> Aerial

> Spaceborne

> Terrestrial Prospects for future data

> Commercial UAVs

An Abundance of Geospatial Data from which to Extract Features and Information

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

Extracting Information from Remotely Sensed Data

Features of Interest

> Vehicles

> Transportation Networks

> Structures

> Natural Features

> Human Activity

Limitations or Opportunities, Given the Data Type

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

Extracting Information from Remotely Sensed Data

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

Object-Based Image Analysis

Building Extraction Methods using Geospatial Data

Pixel by Pixel

Group materials based on their reflectance response per pixel

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> (+) Good for large area-based FX with low-med resolution data

> (-) Poor edge detection without good spectral/spatial resolution; challenging for building extraction

Object-based Image Analysis

Image Pixels

Segmented Objects

Complex Building Features

Merged Segmented

Objects

> 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

Building Extraction Methods using Geospatial Data

For Planning and Risk Identification > Land use planning > Zoning, taxation > Structure inventory > Material Identification

Building Feature Extraction: An Important Aspect for Understanding an Operational Landscape

For Post-event Response > Disaster assessment > Response planning > Reconstruction

monitoring

Buildings are key foundational data layers for GIS and critical to decision analytics

Extracting Features from LiDAR Point Clouds

Features extracted from Point Clouds > Requires thicker point clouds > Based on 3D morphological filters > Proprietary or custom algorithms

DSM DEM Height Model

Features interpreted from derivative raster products > Multi-step process > Feature delineations from

interpolated height values > Use results with object-based FX

Feature identification: 3D point cloud visualization > Manual process, but familiar and expedient

Building Extraction Methods using Geospatial Data

Objective: > Efficiently extract building footprints > Use imagery to glean information about the structures that

will provide situational awareness

Applying Methods to Extract Building Features

Combining Optical and LiDAR Data for Decision Support

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

Applying Methods to Extract Building Features: LiDAR

Use Advanced 3D Algorithms to Process LiDAR Data

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

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

DSM DEM Height Model Building Vectors > Immediate asset inventory > AOIs for additional processing

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 ROI Roof Angle and Slope

ROI Spectral Image Roof Composition

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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

Thank You

© 2013 Exelis Visual Information Solutions, Inc.