E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover
-
Upload
trimble-geospatial-munich -
Category
Technology
-
view
1.200 -
download
1
description
Transcript of E Cognition User Summit2009 Pregesbauer Geo Info Li Dar Basic Landcover
Michael PregesbauerFolie 1
Basic Landcover Classification by LiDAR and Optical Data
eCognition User Summit 2009Munich
November 2009
Michael PregesbauerFolie 2
Contents
• Overview – data resources• Object Generation• Class Definition and Classification• Classification results• remarks on data accuracy and data precision
Michael PregesbauerFolie 3
Why basic landcover classification for the public sector?
• legislative duties (e.g. land use planning, building regulation, sound
wave propagation models)
• planning purposes (e.g. infrastructure networks, soil sealing)
• change detection (development of settlements)
Michael PregesbauerFolie 4
Which Data for Classification
• Digital Orthofotos, 4 Channels (Red, Green, Blue, near Infrared), 12.5cm Ground Sampling Distance
• Digital Terrain Model, 1m Grid width
• Digital Surface Model, 1m Grid width
DOP
DSM
DTM
Michael PregesbauerFolie 5
Aim
Classification of
• Sealed areas– Buildings– Road Network
• Vegetation (Forest Areas)
• comprehensive data set for the whole state [20.000 km²]• stable classified classes (reliability ~ 95%)
Michael PregesbauerFolie 6
Data processing
• Dataset Tiling (2000x2000 Pixel)
• Creation of Initial Objects
• Stitching to Object Primitives
• Classification
Michael PregesbauerFolie 7
Object Primitives byImage Object Fusion
• Object fusion based on a condition:– spectral difference– height difference– border condition
Seed
CandidateCandidate
Michael PregesbauerFolie 8
Creation of Object Primitives
• The class filter allows restricting the potential candidates by their
classification.
• Fitting function threshold allows to select a feature and a condition you want to optimize the fusion.
• Depending on the fitting mode, one or more candidates will be merged with the seed image object.
Michael PregesbauerFolie 9
Classification
Normalized Differenced Vegetation Index
Mean Height Buidlings
Normalized Differenced Vegetation Index (NDVI)
Mean Height Vegetation
Property Class
Mean High as a definite threshold
NDVI as fuzzy function
Class A Class B
Michael PregesbauerFolie 10
Improvement of Buildings
Michael PregesbauerFolie 11
Appraisal of results
Buildings
• ~ 88 % accurately classified
• ~ 9 % classified as elevated objects
• ~ 3 % not classified
Vegetation
• ~ 91 % accurately classified
• ~ 5 % false classified
• ~ 4% not classifiedElevated Objects
Buildings
Vegetation
Michael PregesbauerFolie 12
Misclassification - Example
• Building Objects within Vegetation Areas
• Elevated Objects next to Buildings
Elevated Objects
Buildings
Vegetation
Michael PregesbauerFolie 13
Misclassification - Example
• Shadows
• dark roofs
• edges of buildings
lead to misclassifications at borders of buildings
Michael PregesbauerFolie 14
Misclassification - Example
Object Properties• Mean NDVI > 0• Means nDS 4.7 m• Shadow Index > 0.07
⇒ Object Class: Building
enhanced approach: usage of masks
Michael PregesbauerFolie 15
Additional Mask Layer
RGBi Image Layer
NDVI Layer
non Vegetation Mask
Elevated Objects Mask
Layer arithmetic‘s([Mean nir]-[Mean red])/([Mean nir]+[Mean red])
Layer arithmetic’s1: (NDVI ≥ 0); 0: (NDVI < 0)
Layer arithmetic's 1: (NDVI ≥ 0) and (nDS > 1); 0: (NDVI < 0)
Michael PregesbauerFolie 16
Classification improvement
Michael PregesbauerFolie 17
Results
Buildings
• ~ 94.3 % accurately classified
• ~ 5.2 % classified as elevated objects
• ~ 0.5 % not classified
Vegetation
• ~ 96.1% accurately classified
• ~ 1.1 % false classified
• ~ 2.8 % not classified
Buildings
Vegetation
[Abbildung]
Michael PregesbauerFolie 18
Results
Michael PregesbauerFolie 19
Building Generalization
Michael PregesbauerFolie 20
Results – Building Generalization
• Building Objects are generalized by a bounding box
• export as shape with attribute mean height
Michael PregesbauerFolie 21
Performance Tests
Hardware• 1 Server HP DL380G4 , 4 CPUs• 1,8 Tb working storage
Software• 3 Definiens Server v7.0• 1 Definiens Developer v7.0
Processing Time• 1 processing unit (1000x1250m) = ~ 11min• 1 processing unit (1000x1250m) building generalization (v8.0 beta)
= ~ 25min
Michael PregesbauerFolie 22
Lessons learned
Classification quality depends essentially on the data quality:
• accuracy of the geo-referencation
• spectral quality of the optical data
• filter quality of the LiDAR data
• time lag between data acquisition (LiDAR and optical data)
Classification quality can be enhanced by the
• usage of true orthofotos
• usage of DTM, First- and Last Pulse data
Michael PregesbauerFolie 23
Contact:Michael PregesbauerState Government of Lower AustriaLandhausplatz 1, A-3109 St.PoeltenTel.: ++43(0)2742/9005/13404Mail.: [email protected]
Thanks toChristian Weise [Definiens AG]Gregor Willhauck [Definiens AG]