Til schnur ecognition workshop by KOREC Group

eCognition Day Southampton

October 2013 Trimble ÂŠ2010 Trimble Navigation Limited

eCognition technology in a nutshell

ÂŠ2010 Trimble Navigation Limited

Object Based Image Analysis Why do we see what we see? Building

Human Visual Perception ● Analyzes

groups of pixels

● Incorporates ● Works

context

Terrace

Tile

People

Seam

Head

Body

on multiple scales

4-Oct-13

eCognition in a nutshell Controlled with Rule Set Segmentation

Classification

Context

Abstraction

Result Input Raster Vector Point cloud

• • • • • • •

fuses raster, vector and point cloud data stacks uses pixels, objects and object networks leverages context rules to achieve greater result accuracy knowledge-based, sample-based, machine learning enables 2D, 3D and time series data analysis various export options: raster, vector, report, las automation framework

Raster Vector Point cloud Report

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Core Technology Components Hierarchical Network of Objects

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Object Attributes

Rule Set in CNL (Cognition Network Language)

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eCognition Segmentation - Overview Principles -

multiple scale segmentation supported (image object levels) independent segmentations supported (maps) vector consideration supported (thematic layers) segmentation of pixel level, existing objects, and image regions supported (image object domain)

Overview Primary - Chessboard - Quadtree - Multiresolution

Object Cut - Contrast Split - Contrast Filter - Multi-threshold

Object Fusion - Multiresolution Region Grow - Spectral Difference - Merge Region - Grow Region - Image Object Fusion

Shape Optimization - Shape Split - Border Optimization - Morphology - Watershed Transformation - Pixel-based object resizing

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eCognition Segmentation Algorithms Chessboard Segmentation -

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creates square objects of specified size fast, no spectral awareness typical application: tile scenes; translate vector boundaries into object outlines

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eCognition Segmentation Algorithms Quadtree Segmentation -

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creates variably sized square objects based on heterogeneity variable scale very fast while reasonably reflecting spectral characteristics typical applications: fast extraction of homogeneous features; quick creation of initial objects

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eCognition Segmentation Algorithms Multiresolution Segmentation -

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creates smooth representation of spectrally distinctive regions variable scale, shape and compactness parameters; layer weights can be applied to pixel level or existing image objects best results but computationally intensive typical applications: accurate delineation of distinct features; creating multi-scale object hierarchy

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eCognition Segmentation Algorithms Multiresolution Region Grow -

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fuses existing image objects using the multiresolution approach variable scale, shape and compactness parameters; layer weights typical application: fusion of initial objects created by Quadtree or Multiresolution Segmentation

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eCognition Segmentation Algorithms Spectral Difference Segmentation -

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fuses existing image objects based on spectral similarity variable scale; layer weights typical application: fusion of initial objects created by Quadtree or Multiresolution Segmentation

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eCognition Segmentation Algorithms Image Object Fusion -

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fuses existing image objects based on fitting function fitting function can combine several attributes different fitting modes (e.g. all fitting, best fitting, mutual fitting) seed, candidate, or target optimization typical applications: fusion of initial objects created by Quadtree or Multiresolution Segmentation; optimize objects for roundness, spectral homogeneity, or other attribute

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eCognition Segmentation Algorithms Merge Region -

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merges adjacent image objects of the same class typical applications: minimize number of objects after classification steps; export preparation

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eCognition Segmentation Algorithms Pixel-based object resizing (here: Customized Algorithm Building Generalization) -

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growing and shrinking of image objects based on pixel criteria typical applications: create buffers; generalize objects

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eCognition Classification - Overview

Assign Class -

object labeling using conditions and thresholds

Fuzzy Logic Classification -

pre-defined fuzzy membership functions combination of multiple functions in class descriptions

Sample-based Classification -

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Nearest Neighbor (KNN) Bayes Support Vector Machines (SVM) Classification and Regression Tree (CART) applicable to pixel or image object level trained by samples taken in eCognition, imported samples, or classified image objects

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eCognition LiDAR Project Examples

ÂŠ2010 Trimble Navigation Limited

GeoInfo – Buildings & Vegetation  Uses – legislative duties – planning purposes – change detection

 Data – Digital Ortho Photos – Digital Terrain Model – Digital Surface Model

Lagen Spatial – Automated Land Cover Mapping  Lagen Spatial – Automated Land Cover Mapping  Application for Local Government  Generated GIS Layers – – – – – – –

Building Footprint Pervious / Impervious Land Cover Sealed / Unsealed Roads Vegetation Identification Parks Water / Dams Building Heights

Infoterra - Land Base  

Land cover vector map Framework for use within a GIS system –

–

– – –

Provides unrivalled land cover resolution—to property level/1:5000 scale Enables creation of custom solutions to enable higher level analysis Compatible with existing land cover products Allows 3D analysis as height information built-in Enables cost effective thematic mapping Allows spatial analysis of specified area

USFS / UVM – Urban Tree Canopy Assessment  Urban tree canopy (UTC) assessment  In use by several major US cities to establish their tree canopy goals  Based on LiDAR in combination with CIR data, the application measures: – – –

amount of tree canopy that currently exists amount that could exist Power lines are automatically masked out to not interfere with vegetation

Woolpert – Impervious Surface  Imagery Programs: – – – – – –

Ohio Statewide 105,000km2 Indiana Statewide 96,000km2 Florida Statewide 150,000km2 30cm & 15cm Resolution True / False Color IR LiDAR

 Products: – –

–

―Statewide‖ value added datasets Impervious/Pervious features Agricultural Use Analysis

Blom – Urban Trees  Goal- Delineation of individual trees from laser data  Data – –

Laser data for tree crown delineation (> 2 point / m2) Images for tree species classification

 Results - per tree    

Position (x,y) Height Crown diameter Tree species (from images)

Feature extraction Example Buildings & Vegetation

Input Data & Objectives Digital Orthofotos

Digital terrain model

(RGB+NIR; 0,15 cm; Austrian Sheet Line: 8340x6673 pxl)

(1 m)

Digital surface model based on LiDAR (1 m)

Results

Accuracy  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

 Applied on 60.000 sheet lines (Austrian Map)

Forest Stand Delineation •

•

Features ●

Automatic generation of DSM, DTM and nDSM from LiDAR point cloud

●

Automatic delineation of forest stands based on single tree heights

●

Removal of “irregular” shapes (area, width)

Reference ●

•

Input Data ●

•

Leading Edge Geomatics (LEGeo) LiDAR point cloud (15 pts/m²)

Output ●

Ground

●

Forest ●

•

Height based forest stands

Benefits ●

0m-5m 5 m - 10 m 10 m - 15 m 15 m - 20 m Ground

No preceeding ground/non ground classification needed

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Tree Crown Delineation - LiDAR

Tree crown delineation  LIDAR  Resolution 1.0 m

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Urban Tree Canopy Assessment – LiDAR

 Assessment of existing and potential urban tree canopy  LiDAR + CIR  Resolution: 1 m

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Estimation of Tree Crown Volume – LiDAR

 Crown delineation and volume estimation  LiDAR  Resolution: 0.2 m

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Vegetation around powerlines •

Goal ● Mapping

•

Data ● Laser

•

the trees and shrubs near powerlines

data for vegetation heights (> 1 point / m2)

● Vector

map with powerlines

● Buffer

around powerlines

● Vector

map with buildings

Results ● Vegetation

in the height classes (e.g. ≥ 2.5 m) 4-Oct-13

Vegetation around powerlines

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Powerlines

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Powerlines + buffer

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Powerlines + buffer

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nDSM

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Vegetation > 2.5 meter

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Vegetation > 2.5 meter

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Vegetation > 2.5 meter

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Vegetation around powerlines •

•

Strategy of eCognition rule set ●

Contrast split segmentation

●

Removal of hits on wires and other non-vegetation objects

Low voltage powerlines (isolated) ●

Several projects on low voltage powerlines

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500 to 1000 km powerline per municipality

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Projects are based on existing LiDAR data

Pilot on high voltage powerlines

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Distance of the wire to the ground

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Distance of the vegetation to the wire

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Vegetation height maps 4-Oct-13

Building Changes on Cadastral Maps

•

Features ●

Automatic detection of differences/ changes between cadastral buildings and “reality”

●

GUI for calibration of automatic processing

●

GUI for guided visual result validation

Reference ●

•

Landesamt für Vermessung und Geobasisinformation, Rheinland-Pfalz, Germany

Input Data ●

Ortho photos (RGB + NIR, 0.2 m)

●

DSM + DTM (1 m) from LiDAR

●

Building outlines

©GeoBasis-DE/LVermGeoRP2011-08-31

•

Output ●

New buildings

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Disappeared buildings

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Changed buildings

Remarks ●

©GeoBasis-DE/LVermGeoRP2011-08-31

Successfully tested on different data sources

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Trees, Buildings, Powerline Corridors from LiDAR point clouds 

LiDAR .las files – ALS point cloud, ~ 50 points per m² – Non-filtered point clouds – Filtered point clouds (ground, DEM) not used for this study



Orthophotos – –

RGB 0.1 m ground resolution

3D visualization of point cloud subset

RGB orthophoto subset

Data Import Automation Consistent data structure on file repository

001

002

003

Customized Import

Automatically created projects in eCognition Workspace

Data Import Automation cont.

Elevation Model Derivation 

General Workflow – – –

From the original, unclassified point cloud, surface and elevation models are derived DEM and DSM are used to create a normalized surface model (nDSM) Heights above ground for elevated objects are then used in subsequent analysis steps

high

low

Digital Surface Model (DSM)

Normalized Digital Surface Model (nDSM)

Point Cloud Digital Elevation Model (DEM)

Elevation Model Derivation ď&#x201A;§

Ground point classification â&#x20AC;&#x201C; Classification of ground points based on exclusion of elevated objects (strong elevation edges)

3D visualization of point cloud subset (002_org.las) [points representing ground in pink]

Elevation Model Derivation ď&#x201A;§

DEM creation â&#x20AC;&#x201C; DEM generated using eCognition's "Fill Pixel" inverse distance weighted interpolation

Ground points of point cloud subset (002_org.las) Digital elevation model (DEM), 0.1 m ground resolution

Elevation Model Derivation ď&#x201A;§

DSM creation - Digital surface model (DSM) created from maximum elevations of non-ground points followed by interpolation

Surface points of point cloud subset (002_org.las) Digital surface model (DSM), 0.1 m ground resolution

Elevation Model Derivation 

nDSM calculation – Difference layer providing height above ground information – Slope and Aspect are available as standard procedures as well

Original elevation point cloud subset (002_org.las)

Normalized elevation point cloud subset for nDSM

Buildings 

General idea – Use case investigated: Building roof footprint using the original, unfiltered point cloud plus nDSM derived from that – General processing steps: Generalization of nDSM layer – find elevation edges – segmentation – classification of elevated objects – exclusion of powerlines – exclusion of elevated vegetation – building seed classification and refinement – generalize building shape

Buildings

nDSM

nDSM generalized

Elevation edges

Segmentation

Elevated objects classification

Power lines exclusion

Elevated vegetation exclusion

Building seed classification

Building shape generalization

Powerlines 

General idea – Use case investigated: Powerline corridor delineation from LAS point cloud and nDSM – RGB image only used for visualization – General processing steps: Elevation edge / line detection on nDSM – classification of potential powerline fragments – exclusion of non-linear / otherwise unlikely powerline fragments – designation of generalized powerline corridor

Powerlines

RGB image for visualization

nDSM from point cloud

Potential powerline fragments

Generalized powerline corridor

Line / elevation edge detection

Elevated Vegetation / Trees 

General idea – Use case investigated: Elevated vegetation and individual tree delineation from LAS point cloud and nDSM – General processing steps: Shadow classification – vegetation classification – elevation model optimized segmentation – designation of individual tree seeds – tree seed growing to individual tree footprints

Elevated Vegetation / Trees

RGB for visualization

nDSM from point cloud

nDSM generalized

Elevation model optimized segmentation

Designation of indidual tree seeds

Grow to individual tree crown outline

Point Cloud Processing in eCognition

ÂŠ2010 Trimble Navigation Limited

Object Based Point Cloud Analysis

•

Point Cloud handling 

Adapt point cloud raster resolution and extent



Virtually merge multiple point clouds

Point Cloud features and algorithms 

Extended descriptive objects and scene statistics



Extended point cloud filtering parameters

Mean/Mode

Quantile 90 4-Oct-13

Workflow Example Object Based Point Cloud Analysis Workflow integration ●

Load point clouds in *.las format

●

Export classified point clouds in *.las format

2D Segments

Selective 3D ●

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Combine 2D segments with 3D statistical attributes

3D Points

Derive information directly from the point cloud

Streamlined analysis ●

Directly combine raster imagery and point cloud information

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Integrate vector, raster and point cloud data

3D Attributes

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Workflow Example Object Based Point Cloud Analysis Point Cloud Features are descriptive structure statistics for Objects calculated from x/y, Intensity and Elevation information from the Points

Return Pulse Object

Returns

Specifies which points should be used for calculation

All Returns

First Returns

Last Returns

Modes Specifies which algorithm should be used for calculation

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Average, Standard Deviation, Minimum, Maximum, Median, Mode

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Workflow Example Object Based Point Cloud Analysis Trimble Harrier System •

Combines a wide-angle full waveform digitization laser scanner with a Trimble Aerial Camera (TAC)

•

TAC = compact, high-performance 80 MP medium format aerial camera, trouble-free operation, and advanced features such as forward motion compensation



System for corridor mapping and aerial survey

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Workflow Example Object Based Point Cloud Analysis Trimble Harrier System (Simultaneous airborne imaging and laser scanning) Create objects on image information

Classify objects on point cloud features (a)

Elevated Vegetation = multiple-return pulses

(b)

Vegetation height = max first return – min last return

 Directly combine raster imagery and point cloud information

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Land Mobile Feature Extraction I •

“Top Down” and “Camera View” analysis 

Extended capabilities for mobile terrestrial systems (i.e. MX8)



Perspectives for horizontal and vertical Feature extraction

Top Down View

Camera View

Classification

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Land Mobile Feature Extraction II

•

3D Export 

3D line extraction from classified point clouds



Export point clouds in Trident LAS 2.0 format

Trident Analyst support 

Create eCognition projects based on Trident data sets



Load point clouds in Trident LAS 2.0 format

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Application Example Trimble MX8 •

Vehicle-mounted system

•

Combines a dual laser scanner with multiple cameras

 System

for mobile mapping

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Application Example

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Application Example

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Thank You

ÂŠ2010 Trimble Navigation Limited