Dr waldemar krebs trimble by KOREC Group

eCognition End-to-End Remote Sensing and Image Analysis Solutions and Methods for Cadaster Update Dr. Waldemar Krebs Trimble Southampton

eCognition History 1995

Research â&#x20AC;&#x161;Think-Tankâ&#x20AC;&#x2DC; created by Physics Nobel Prize Laureate Prof. Dr. Gerd Binnig

1998

Cognition Network Technology; 20+ patents

2000

Software Commercialization

2004

2010

Enterprise Image Intelligence; focus on Life Sciences and Earth Sciences

Trimble Navigation / Trimble Geospatial

Worldwide Customers MOMRA

es to information

AERIAL Laser & photogrammetry

Point clouds & images

Collect

Process

Analyze

Information production

LAND

For further information please contact:

Volker Zirn Regional Sales Representative Trimble GeoSpatial

Phone: +49 711 228810 Email: Volker_Zirn@trimble.com

On web: www.inpho.de www.trimble.com

Trends in Earth Observation Increasing need for detailed, up-to-date information as a basis for planning and decision making –

Increasing data volume

–

Increasing data complexity

–

Increasing demand for geo information

–

Data+Details+Value+Complexity

Manual interpretation becomes a major bottleneck



Automation

Resources

Multiresolution Image Segementation pixel

medium

fine

coarse

Object generation on multiple scales to adress differently scaled classification task within one project

Build up a hierarchical network of image objects, which allows the representation of the image information content at different resolutions (scales) simultaneously.

Dynamic image analysis workflow Controlled with Rule Set Segmentation

Input Raster Vector Point cloud

Classification

Context

Abstraction

Result Raster Vector Point cloud Report

• fuses raster, vector and point cloud data stacks for processing • uses pixels, objects and object networks for superior analysis • leverages context rules to achieve greater result accuracy • enables 2D, 3D and time series data analysis

eCognition Software Suite eCognition Developer Is the development environment for object-based image analysis.

eCognition Architect Provides an easy to use front end for non-technical professionals allowing them to leverage eCognition technology.

eCognition Server Provides a processing environment for the batch execution of image analysis jobs.

eCognition Software Suite  eCognition Suite  eCognition Developer 8  eCognition Architect 8  eCognition Server 8

eCognition Software Suite  eCognition Suite  eCognition Developer 8  eCognition Architect 8  eCognition Server 8  eCognition Server Power Bundle UNTIL END OF NOVEMBER BUY 4 FOR THE PRICE OF 1

Whatâ&#x20AC;&#x2122;s New in eCognition 8.9 Christian Weise

eCognition 8.9 Highlights Land Mobile Feature Extraction Leverage eCognition to extract features from land mobile data in different views to combine best perspectives for both horizontal and vertical information extraction Extended Point Cloud Handling Increase analysis performance by applying temporary point cloud concept to enable efficient data access and processing without overwriting of source data Improved Rule Set Flexibility Build flexible rule sets or applications using all information types in different context & leverage state-of-the-art "Random Trees" machine learning algorithm

Land Mobile Analysis

Point Cloud Handling

8.9 Rule Set Flexibility

Land Mobile Feature Extraction •

Multi-perspective analysis 

Creates top down perspective map based on a camera view perspective map



Combines different perspectives in the feature extraction process to use the best view of the data for both horizontal and vertical analysis



Fuse image information (i.e. color) with point cloud information (i.e. elevation) into a streamlined multiperspective analysis

Camera Perspective

Top Down Perspective

Improved Rule Set Flexibility •

•

Random Trees 

Fully integrated Random Trees classifier (following Leo Breiman’s theory of random forests: http://www.stat.berkeley.edu/users/breiman/RandomForests/)



Remove need to work with external algorithms to save time and create a simple workflow

Rule Set Type Conversion 

Converting rule set item types to text and vice versa



Using rule set items independent of the type, i.e. Scene ID  Level name



Flexible information Sceneexchange Feature in Rule Sets or Architect solutions Scene Feature Scene Feature Name Object Double Feature Object Feature Name Map Name Class Name Layer Name Level Name

String

Scene Feature Name Object Double Feature Object Feature Name Map Name Class Name Layer Name Level Name

FloodMapper User Interface  Automated and semiautomated rule sets are combined in a library.

 Use of widgets together with a quick visualization of results and “undo” option allows for an intuitive selection of the parameters.

 Object information tools and graphics allow for deeper analysis of the data, if required.

Example semi-automated flood mapping

Just One Click

Segmentation

Pre-event data

Pre-event water detection

Post-event data

Just One Click

Post-event water detection

Change detection

Integration of Final Results into GIS, Cartography and Mapping

The results of the classification can be exported and directly integrated into any GIS environment

Summary  TerraSAR-X FloodMapper  Provides the customer with a fully-fledged solution to map flooded areas on the basis of TerraSAR-X data (in case TerraSAR-X pre-event data is not available, optical EO data can be used)  Is easy to install and operate  TerraSAR-X data are highly suited to map flooded areas (cloud coverage, geolocation accuracy etc.)  Data continuity is ensured via two satellites in orbit (TerraSAR-X and TanDEM-X)  Final products are according to general standards or customizable to specific requirements

References  Astrium GEO pricelist

http://www.astrium-geo.com/files/pmedia/public/r463_9_itd-0508-cd-0001-tsx_intern

 Astrium GEO product examples http://www.astrium-geo.com/en/864-emergency-response-to-floods-in-andalusia

 Herrera Cruz, V.; Müller, M. & Weise, C. (2010): Flood extent mapping based on TerraSAR-X data. PFG 2010 / 6, pp. 475-488.

 Herrera-Cruz, V., F. Koudogbo (2009): TerraSAR-X rapid Mapping for flood

events. ISPRS, Commission VI, WG VI/4. http://www.ipi.uni-hannover.de/fileadmin/institut/pdf/isprs-Hannover2009/Herrera-Cr

 Trimble eCognition Developer Reference Manual, Version 8.64.1  Trimble eCognition User Guide, Version 8.64.1

Date - 22

Global Forest Map 

Customer – –



Background –

–



– –



Project Forest Carbon Tracking to monitor increases and decreases in forested area around the world Monitor forest mass to prevent deforestation

Scope/Solution –



Japan Aerospace Exploration Agency (JAXA) In co-operation with Aberystwyth University

Automatic forest/non-forest classifcation on SAR (Synthetic Aperture Radar) data 86,000 scenes covering the globe High transferability and reliability of solution

Data –

ALOS PALSAR

–

L-band

–

10 m

Results –

World wide Forest/Non-Forst map of 2009 (10 m resolution)

Cadastral Change Detection â&#x20AC;&#x201C; Landesamt fĂźr Vermessung und Geobasisinformation, Rheinland-Pfalz, Germany

Cadastral Change Detection  Project PI – Cadaster Administration Rheinland-Pfalz – Landesamt für Vermessung und Geobasisinformation, Rheinland-Pfalz, Germany

 Background – Excellent precise cadaster – But too “old” – Change detection based on aerial data time consuming (20.000 images)

 Goal – Semi-Automatic detection of changes – Optional adjustment of automatic processing – Consideration of changed input data – Definition of change  Area  Tolerance

– Easy to Use User interaction by means of easyto-use graphical user interfaces (GUI)

Existing Buildings Development Area

Cadastral Change Detection  Solution – Automated detection of building changes in cadastral data (building footprints vs. aerial photography and height data) – Extracted information – Buildings  New buildings  Changed buildings  Non mapping-relevant

– Vegetation  Elevated vegetation  Ground vegetation

– GUI for optional manual adjustment of automatic processing rules – GUI for visual quality assurance/control (QA/QC) and manual editing – GUI based on user requirements

 Data – RGB + NIR ortho-photos (0.2 m) – LiDAR DSM/DTM (1 m) – Building footprints (Vector)

Resultsâ&#x20AC;&#x201C; Architect Solution

Result– Example „448_456“ Change Classification Detection DSM RGB DTM ALK

Building and Vegetation Extraction Amt der Niederรถsterreichischen Landesregierung (Government Lower Austria), Austria

Building and Vegetation Extraction  Project PI – Amt der Niederösterreichischen Landesregierung (Government Lower Austria), Austria

 Background – Environmental Noise Directive of the European Parliament and Council requires up-to-date information about elevated objects for modeling sound-wave propagation – 20.000 RGB/NIR + Lidar (Lower Austria) – 60.000 RGB/NIR + Lidar (Austria) – Highly cost efficient and standardized

 Goal – Automatic LiDAR/RGB-NIR data processing – Automatic detection and classification of elevated objects – Buildings – Elevated Vegetation

Results – Accuracy & Next Steps  94,3 % for Buildings  96,1 % for Vegetation Verification area: ca 200 km²; Accuracy is the measure of "true" findings (true-positive + true-negative) divided by all test results

 Algorithm tested and works also on data – – – – – –

Australia (on 0.15m resolution, 1m dsm/dtm) USA (on 0.25m resolution, 1m dsm/dtm) Germany (on 0.15m resolution, 1m dsm/dtm) South Africa (on 0.5m resolution, 2m dsm/dtm)! Hungary (on 0.5m resolution, 2m dsm/dtm)! …

WHAT CAN BE DONE WITH eCognition WITH NEW HIGH RESOLUTION SATELLITE DEM‘s??? Outlook into the VERY near future

IRS-P5 Cartosat-1 Two cameras: PAN-Fore & PAN-Aft +26° and -5° tilt along the track Nominal B/H ratio 0.62 27 k

Swath 27 km 10 bit resolution Spatial resolution 2.5 m 30 k

8 35

Operational since May 2005

Turkey: Ankara in 3D

Euro-Maps 3D with 5 m post spacing

ÂŠ 2013, Euromap, includes Antrix material

ÂŠ 2013 by Euromap Satellitendaten-Vertriebsgesellschaft mbH, Kalkhorstweg 53, 17235 Neustrelitz, Tel.: +49 3981 4883 0, e-mail: info@euromap.de

Turkey: Ankara in 3D

2.5 m PAN-Aft Orthoimage of Ankara draped over Euro-Maps 3D

ÂŠ 2013, Euromap, includes Antrix material

ÂŠ 2013 by Euromap Satellitendaten-Vertriebsgesellschaft mbH, Kalkhorstweg 53, 17235 Neustrelitz, Tel.: +49 3981 4883 0, e-mail: info@euromap.de

Standard Product

Tri-StereoDSM a Very High Resolution Digital Surface Model from WorldView, GeoEye or PlĂŠiades Triple Stereo Imagery

in cooperation with:

Standard-Product Tri-STEREO DSM

ORTHOimage

DigitalSurfaceModel

NUMber of Matches

STD of Measures

All product layers have 0.5m resolution and same geometry. Watermask in NUM Layer or separately.

in cooperation with:

Standard-Product Tri-STEREO DSM

Specifications: Horizontal Resolution

0,5m

Vertical Resolution

0,1m

Horizontal Reference

UTM

Vertical Reference

WGS84/EGM2008

Horizontal Accuracy (absolut)

CE90 < 2,5m (no GCI)

Vertical Accuracy (absolut)

LE90 < 3,5m (no GCI)

Format

GeoTIFF (32bit)

Frame

ca. 15x15kmÂ˛

Standard-Product Tri-STEREO DSM

Results – Accuracy & Next Steps  ? % for Buildings - has to be tested in near future  ? % for Vegetation - has to be tested in near future  Existing Algorithms tested and works on aerial data with 0.15m, 0.25m, 0.5m RGB+IR and 1-2m resolution DSM/DTM  New satellite data are in the same resolution range  IF DEM available  high level on automation possible for building extraction  Also forest extraction can be significant improved  eCognition and new satellite based DEM+Orthoproducts give a perfect combination for areas without access to aerial images

Download Rule Set & Data available at the eCognition Community

eCognition Community  

Part of the eCognition website (http://www.ecognition.com) Provides an integrated environment for Discussions Upload & Download Rule Sets and Guided Tours (Rule Set Exchange) – Product Feedback (eCognition Ideas) – Prototypes (eCognition Labs) – User Guide (Wiki) along with social networking facilities – –

(Download Link)



7000+ Registered Users

Thank You

ÂŠ2010 Trimble Navigation Limited