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Transcript of Historical Airphoto Processing (HAP) Powered by Geomatica
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Historical Airphoto Processing
Technical Overview Presentation
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Presentation Overview
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Historical Airphoto Processing (HAP) SystemBackground – Historical Imagery
Traditional Approach – Correcting Historical Imagery
Value Proposition
HAP System – Details
Summary
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BackgroundHistorical Imagery
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Historical Airphotos - Background
Aerial photography records the ever changing cultural features on the Earth’s surface
Provide valuable information for a number of applications
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Historical Airphotos - Applications
Environmental assessment– Wetlands, flooding, hurricane, earthquake
Climate change studies– Biomes, extents, coastlines
Land use planning– Expansion, densification, transformation, construction
Forestry management– Growth, clearing, re-planting
Oil & Gas– Wellhead sites, pipeline scars
Archaeology and Cultural Heritage– Conservation, restoration, demarcation
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Historical Airphotos – The Need
Millions of historical (analog) airphotos exist in print or film form– If georeferencing can become cost effective, there
are a number of valuable uses for the imagery
Traditional approaches for orthorectifying historical airphotos are costly, time consuming and at times ineffective– Major manual effort to collect GCPs
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Traditional Approach
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Traditional Approach
Collect GCPs manually Standard automatic GCP collection techniques
are not well tuned
Usually requires 3-4 well distributed GCPs
Manual GCP collection is a major production bottleneck
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Traditional Approach
What is the problem?
1. Automatic methods often fail Missing camera information leads to poor initial models
Poor image quality and initial georeferencing hinders automation
2. Manual collection is difficult and costly Manual GCP collection is a time consuming task
Land change can be significant
Scratches on film can create artefacts
Poor quality imagery makes it difficult to identify features
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Poor Initial Georeferencing
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941m (1882px)
Reference
Raw
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Land Cover Change
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1991
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2011
Land Cover Change
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Scratches and Damage
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Poor Radiometry and Contrast
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Scanning Artifacts
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The Solution – PCI’s HAP System
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Semi-automated solution for generating ortho-mosaics from historical airphotos
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HAP SystemValue Proposition
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HAP Value Proposition Increased Production• The HAP system can more than double production
capacity
Increased Efficiency• The multi-pass automatic GCP and Tie Point collection
reduces manual effort
High Accuracy• Accurate ortho-mosaics that often exceed the quality
produced by manual techniques• In many cases can handle poor interior model information better than
manual approach
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HAP Features & Benefits
Features Benefits
Automated Fiducial Collection Reduced labor, high accuracy
Automated GCP/TP Significantly Reduced labor, high accuracy
Ortho-Mosaic – automated cutline and color balancing
High quality mosaics in less time
Works with multi-source and varying quality historical imagery
Rescue your valuable data
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Some Metrics
Performance Information (Hours)Total Manual 2 15 3
Total Automated 16 46 4
Total Time 18 61 7
Accuracy Information (RMSE)Initial Accuracy 155m 254m +300m
Ortho Accuracy 5.7m 9.3m 5.8m
About DatasetsDataset 1 2 3Difficulty Easy Difficult Difficult
# of Images 266 278 50
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Case Study
Project Details
Processing DetailsTraditional Approach HAP System
GCP Collection Manual Automatic
Tie Point (TP) Collection Automatic Automatic
Quality Assurance Manual (TPs only) Manual
Manual Labour (Hours) 24 8
Project Turn Around (Days) 3 Days 1.5 Days
- 130 raw aerial images acquired over a large urban area in 1954- Reference image of urban area acquired in 2011- Population grew by more than 10 million people between 1954 and 2011
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Case Study
The same customer performed 7 additional tests over two areas in Asia Minor.
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Case Study
Project # of PhotosProcessing time (Hours)
Accuracy(CEP 90)
001-1946 122 16.5 ± 8 m
001-1954 117 14.7 ± 6 m
001-1972 90 10.5 ± 6 m
001-1993 87 9.2 ± 5 m
002-1975 69 10.8 ± 7 m
002-1991 73 10.0 ± 6.5 m
002-1999 80 9.0 ± 5 m
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Case Study
“All ortho-photo projects performed by the operator through conventional methods were found to be three times slower than the same processes performed on the HAP System.”
- HAP customer
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HAP SystemDetails
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HAP Interface
Consists of 3 easy to use panels
1123
Panel #1 Specify ingest parameters and
execute
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HAP Interface
Consists of 3 easy to use panels
2123
Panel #2 Initial Georeferencing step panel
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HAP Interface
Consists of 3 easy to use panels
3123
Panel #3 Coarse and Fine
Alignment panel
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System Layout
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Processing Systems QA/QC Machines
Network
Processing Systems for automated tasks QA/QC Machines for inspection and editing Simple network interface for accessing data
remotely
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Workflow Overview
Innovative Multi-Pass Image AlignmentAchieve Ortho accuracy through iterative GCP and Tie Point collection
Manual Quality Check of GCPs and Tie Points
Processing tasks are highly automated scripts
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Historical Airphoto Workflow
Data Prep Data Ingest
QA GCPs/TPs
QA GCPs/TPs
Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Optional Path
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Data Preparation
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Assess Input files• Input Images, Reference Image and DEM on Processing Machine
Create Metadata File • Can customize how ancillary information is fetched & assembled• Metadata only requires 5 pieces of ancillary information
Data Prep Data Ingest Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Manual
Semi-Auto
What is Data Prep…
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Data Preparation
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
MetadataAssess Input files
1. Focal Length
2. Approx. scene center coordinates or way to approximate them (i.e. flight-line)
3. Approx. flying height
4. Print dimension (i.e. 9” x 9”)
A custom script can often be developed
to assemble the data
1. All images part of a continuous block
2. All images in a given project have same fiducial mark (i.e. edge)
3. Appropriate DEM
4. Appropriate Reference Image (i.e. NAIP)
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Ingest Data • Input imagery & reference data is read into HAP and converted to working formats
(PIX)• Calculate nominal Exterior Orientation
Collect Fiducial Marks • Automatic fiducial mark collection from a template image• Automation success depends on image quality
Data Ingest
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Automatic
ManualSemi-AutoAutomatic
What is Data Ingest…
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Data Ingest
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Ingestion Process Initial Accuracy
• Ingest Data into working format (PIX)
• Calculate nominal georeferencing from scene centers and metadata
941m (1882px)
Reference
Raw
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Coarse Alignment
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Data Prep Data Ingest Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Coarse Alignment
GCP Collection• Attempts to collect hundreds GCPs• Automatically removes statistical blunders
Bundle Adjustment• Automatic Tie Point collection and removal• Computes new math model (coarse model) often based on
100s of GCPs and Tie Points collected for each image
What is Coarse Alignment…Initial pass to improve positional and orientation accuracy
Automatic
Automatic
Control
Initial Alignment
Coarse Alignment
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Coarse Alignment
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Data Prep Data Ingest Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Coarse Alignment
Improved accuracy suitable for Fine Alignment run
GCPs & Tie Points Improved Accuracy
152m (304px)
Reference Coarse
• 10px < RMSE < 30px
• Evenly distributed GCPs/TPs
• Often very dense distribution
+ GCPs+ Tie Points
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QA GCPs/TPs
QA Coarse Alignment
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Data Prep Data Ingest Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Coarse Alignment
Review GCPs and Tie Points• Review distribution – Less important after Coarse Alignment• Identify “Island” subsets – Group of images tied together with no GCPs
If Required, Edit GCPs and Tie Points• Delete blunder GCPs & Tie Points• Collect GCPs on “Island” subsets
What does Coarse Align QA Involve…
Manual
Manual
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Fine Alignment
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
GCP Collection• Uses improved model from Coarse Alignment • Searches for GCPs with lower error
Tie Point Collection (Bundle Adjustment)• Searches for Tie Points with lower error
What is Fine Alignment…Create final model used to generate ortho images
Automatic
Automatic
Control
Coarse Alignment
Fine Alignment
Initial Alignment
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QA GCPs/TPs
QA Fine Alignment
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Data Prep Data Ingest Coarse Alignment
Generate Ortho-Mosaic
Manual
Semi-Auto
Automatic
Fine Alignment
Review GCPs and Tie Points• Review distribution – More important after Fine Alignment
If Required, Edit GCPs and Tie Points• Delete blunder GCPs• Prune and/or add GCPs to areas with poor distribution
What does Fine Align QA Involve…
Manual
Manual
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Orthorectification
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Manual
Semi-Auto
Automatic
Ortho Generation
Mosaic Generation
Mosaic PreparationGenerate
Ortho-Mosaic
Reference
OrthoHigh speed orthorectification
<0.5m (<1px)
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Mosaic Preparation
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Manual
Semi-Auto
Automatic
Ortho Generation
Mosaic Generation
Mosaic Preparation
Generate Ortho-Mosaic
Automatic Cutline Generation• High quality results• Retain mostly nadir imagery with cutline
constraints
Automatic Color balancing• Image Normalization (Hot Spot Removal)• Variety of high quality color balancing
algorithms
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QA Cutlines and Color Balancing
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Manual
Semi-Auto
Automatic
Ortho Generation
Mosaic Generation
Generate Ortho-Mosaic
QA Cutlines & ColorMosaic
Preparation
Multi-user Mosaic Editing• Multiple users can edit the same mosaic at
the same time from different computers
Advanced Mosaic Tools• WYSIWYG Viewer• Fast Redraw tools for cutlines• Manually adjust color balancing
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Mosaic Generation
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Data Prep Data Ingest Coarse Alignment
Fine Alignment
Manual
Semi-Auto
Automatic
Ortho Generation
Mosaic Generation
Generate Ortho-Mosaic
QA Cutlines & ColorMosaic
Preparation
Generate accurate seamless mosaics with OrthoEngine or GXL
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HAP SystemSummary
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The HAP System Summary
In response to market demand, PCI has developed new technologies to address the technical and operational difficulties of historical airphoto processing
Semi-automated approach reduces labour costs for large projects Iterative automatic GCP/Tie Point collection
Can double production Designed for taking in large projects 50-1000+
images per project
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Find out more about HAP
Website
Historical Airphoto Processing Website
PDF Whitepaper
YouTube PCI Tech TV HAP Commercial Interview with Lead HAP developer
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Private and Confidential
Contact PCI Geomatics
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www.pcigeomatics.com