cheriemuleh-131114155758-phpapp02.pdf

8/21/2019 cheriemuleh-131114155758-phpapp02.pdf

1/18

Usando ENVI para

extraer elementos

importantes desdeimágenes satelitales y

datos LiDAR

Cherie [email protected]

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.


2/18

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


3/18

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


4/18

Valuing Remotely Sensed Data as a Source for Features

Imagery is not just a base map, but a source of rich informationthat geospatial analysts can use to solve complex problems.

> Provide data availability over broadand inaccessible areas

> Improve timeliness of data acquisition

> Potentially greater accuracy

> Automated feature extraction for

reduction in manual digitization

> Advanced geospatial analysis usingspectral image properties


5/18

Extracting Information from Remotely Sensed Data

Features of Interest

> Vehicles

> TransportationNetworks

> Structures

> Natural Features

> Human Activity

Limitations or

Opportunities,

Given the Data Type


6/18

Needs for Feature Extraction

> Increased availability of high-

resolution images

> Manual digitization

> Semi-automated solution ishighly desired

Applications

> Defense and Security

> Transportation

> Urban planning and mapping

Extracting Information from Remotely Sensed Data


7/18

What is an object?

• An object is a region of interest withspatial, spectral (brightness and color),and/or texture characteristics that definethe region

• Pixels are grouped into objects, insteadof single pixel analysis

• May provide increased accuracy anddetail for classification purposes

Object-Based Image Analysis


8/18

Building Extraction Methods using Geospatial Data

Pixel by Pixel

Group materials based on their

reflectance response per pixel

0.5

0.0

1.0

0.5

0.0

1.0

1 2 3 4 5 6

0.5

0.0

1.0

R

e f l e c t a n c e

Band

0.5

0.0

1.0

0.5

0.0

1.0

1 2 3 4 5 6

0.5

0.0

1.0

R

e f l e c t a n c e

Band

Soil

Veg

Water

1

65

43

2

ImagePixels

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

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


9/18

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



10/18

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


11/18

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



12/18

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 tocapture building footprints

> Conduct image processing routines usingbuildings as regions of interest

Combine the best of

what LiDAR and image

processing have to offer


13/18

Applying Methods to Extract Building Features: LiDAR

Use Advanced 3D Algorithms to Process LiDAR Data


14/18


3D LiDAR Extraction Vector and Raster Products

Classified Point Cloud

Trees

> Location, Elevation, Height, Radius

Buildings

> Location, Perimeter Vectors, Roof FaceVectors

Power Lines

> Power Line Vectors, Power Pole List, PowerLine Attachment Points

Terrain

> Digital Surface Model (Grid and TIN),Digital Elevation Model, Ground contours

Valuable GIS

Data Layers


15/18


Leverage Building Footprints and Elevation Products

Determine Height Model

> Raster data for additionalprocessing/awareness ofobjects in the area

DSM DEM Height ModelBuilding Vectors

> Immediate asset inventory> AOIs for additional processing


16/18

Applying Methods to Extract Building Features: Optical

Image Analysis Methods Using LiDAR-derived Products

Topographic Modeling

> Use raster height modeldata to determine roofslope & aspect on buildings

Spectral Analysis

> Apply object-based FX tomulti/hyperspectralimagery, using building

footprint ROIs> Capture additional spectral,textural, spatial attributesfor additional analysisopportunities

Height Model ROI Roof Angle and Slope

ROISpectral Image Roof Composition


17/18

October 23, 2013 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

Documents

cheriemuleh-131114155758-phpapp02.pdf