Historical Airphoto Processing (HAP) Powered by Geomatica
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Historical Airphoto Processing Using modern, automated image correction methods to tap into valuable historical imagery. Historical aerial photography archives contain valuable information that remains untapped. Digitally scanned and properly geo-referenced historical aerial imagery can bring this information to life, making it possible to analyze/visualize the historical information in modern GIS systems. These historical images can reveal hidden patterns, provide a deeper understanding of changes over time thus leading to better decision making. PCI Geomatics offers a customized solution to automate the correction of historical imagery. With it, users can properly prepare historical data and set up workflows that will create perfectly aligned and orthorectified mosaics for use in numerous GIS applications. Start realizing the value of your vast archives of historical airphoto imagery today and turn hundreds / thousands of archive images into GIS ready digital mosaics.
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Historical Airphoto Processing (HAP) Powered by Geomatica
- 1. Historical Airphoto Processing Powered by Technical Overview Presentation
- 2. Presentation Overview Historical Airphoto Processing (HAP) System Background – Historical Imagery Traditional Approach – Correcting Historical Imagery Value Proposition HAP System – Details Summary Page 2 Copyright © 2013 PCI Geomatics
- 4. Historical Airphotos - Background Aerial photography records the ever changing cultural features on the Earth’s surface Provide valuable information for a number of applications Page 4 Copyright © 2013 PCI Geomatics
- 5. 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 Page 5 Copyright © 2013 PCI Geomatics
- 6. 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 Page 6 Copyright © 2013 PCI Geomatics
- 8. 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 Page 8 Copyright © 2013 PCI Geomatics
- 9. 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 Page 9 Copyright © 2013 PCI Geomatics
- 10. Poor Initial Georeferencing Page 10 Copyright © 2013 PCI Geomatics
- 11. Land Cover Change 1991 Page 11 Copyright © 2013 PCI Geomatics
- 12. Land Cover Change 2011 Page 12 Copyright © 2013 PCI Geomatics
- 13. Scratches and Damage Page 13 Copyright © 2013 PCI Geomatics
- 14. Poor Radiometry and Contrast Page 14 Copyright © 2013 PCI Geomatics
- 15. Scanning Artifacts Page 15 Copyright © 2013 PCI Geomatics
- 16. The Solution – PCI’s HAP System Semi-automated solution for generating ortho-mosaics from historical airphotos Page 16 Copyright © 2013 PCI Geomatics
- 18. 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
- 19. 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
- 20. Some Metrics About Datasets Dataset 1 2 3 Difficulty Easy Difficult Difficult # of Images 266 278 50 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
- 21. Case Study Project Details - 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 Processing Details Traditional Approach GCP Collection Tie Point (TP) Collection Quality Assurance Manual Labour (Hours) Project Turn Around (Days) HAP System Manual Automatic Automatic Automatic Manual (TPs only) Manual 24 8 3 Days 1.5 Days
- 22. Case Study The same customer performed 7 additional tests over two areas in Asia Minor.
- 23. Case Study Project # of Photos Processing time (Hours) Accuracy (CEP 90) 001-1946 122 16.5 ±8m 001-1954 117 14.7 ±6m 001-1972 90 10.5 ±6m 001-1993 87 9.2 ±5m 002-1975 69 10.8 ±7m 002-1991 73 10.0 ± 6.5 m 002-1999 80 9.0 ±5m
- 24. 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
- 26. HAP Interface Consists of 3 easy to use panels Panel #1 Specify ingest parameters and execute 26 Copyright © 2013 PCI Geomatics
- 27. HAP Interface Consists of 3 easy to use panels Panel #2 Initial Georeferencing step panel 27 Copyright © 2013 PCI Geomatics
- 28. HAP Interface Consists of 3 easy to use panels Panel #3 Coarse and Fine Alignment panel 28 Copyright © 2013 PCI Geomatics
- 29. System Layout Processing Systems for automated tasks QA/QC Machines for inspection and editing Simple network interface for accessing data remotely Network Processing Systems QA/QC Machines Page 29 Copyright © 2013 PCI Geomatics
- 30. Workflow Overview Innovative Multi-Pass Image Alignment Achieve Ortho accuracy through iterative GCP and Tie Point collection Manual Quality Check of GCPs and Tie Points Processing tasks are highly automated scripts Historical Airphoto Workflow Optional Path Data Prep Data Ingest Coarse Alignment Fine Alignment Generate Ortho-Mosaic Manual Semi-Auto Automatic QA GCPs/TPs QA GCPs/TPs Page 30 Copyright © 2013 PCI Geomatics
- 31. Data Preparation Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Fine Alignment Generate Ortho-Mosaic What is Data Prep… Assess Input files Manual • Input Images, Reference Image and DEM on Processing Machine Create Metadata File Semi-Auto • Can customize how ancillary information is fetched & assembled • Metadata only requires 5 pieces of ancillary information Page 31 Copyright © 2013 PCI Geomatics
- 32. Data Preparation Manual Semi-Auto Data Prep Data Ingest Automatic Assess Input files Coarse Alignment Fine Alignment Generate Ortho-Mosaic Metadata 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 1. Focal Length 4. Appropriate Reference Image (i.e. NAIP) 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”) Page 32 Copyright © 2013 PCI Geomatics
- 33. Data Ingest Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Fine Alignment Generate Ortho-Mosaic What is Data Ingest… Ingest Data Automatic • Input imagery & reference data is read into HAP and converted to working formats (PIX) • Calculate nominal Exterior Orientation Collect Fiducial Marks Automatic Semi-Auto Manual • Automatic fiducial mark collection from a template image • Automation success depends on image quality Page 33 Copyright © 2013 PCI Geomatics
- 34. Data Ingest Manual Semi-Auto Data Prep Data Ingest Automatic Ingestion Process Coarse Alignment Fine Alignment Generate Ortho-Mosaic Initial Accuracy • Ingest Data into working format (PIX) • Calculate nominal georeferencing from scene centers and metadata Page 34 Copyright © 2013 PCI Geomatics
- 35. Coarse Alignment Manual Semi-Auto Data Ingest Data Prep Automatic Coarse Alignment Fine Alignment Generate Ortho-Mosaic What is Coarse Alignment… Control Initial pass to improve positional and orientation accuracy GCP Collection Coarse Alignment Automatic • Attempts to collect hundreds GCPs • Automatically removes statistical blunders Bundle Adjustment Automatic • 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 Initial Alignment Page 35 Copyright © 2013 PCI Geomatics
- 36. Coarse Alignment Manual Semi-Auto Data Prep Data Ingest Automatic GCPs & Tie Points Coarse Alignment Fine Alignment Generate Ortho-Mosaic Improved Accuracy + GCPs + Tie Points • 10px < RMSE < 30px • Evenly distributed GCPs/TPs Improved accuracy suitable for Fine Alignment run • Often very dense distribution Page 36 Copyright © 2013 PCI Geomatics
- 37. QA Coarse Alignment Manual Semi-Auto Data Prep Data Ingest Automatic Coarse QA Alignment GCPs/TPs Fine Alignment Generate Ortho-Mosaic What does Coarse Align QA Involve… Review GCPs and Tie Points Manual • 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 Manual • Delete blunder GCPs & Tie Points • Collect GCPs on “Island” subsets Page 37 Copyright © 2013 PCI Geomatics
- 38. Fine Alignment Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Fine Alignment Generate Ortho-Mosaic Control What is Fine Alignment… Create final model used to generate ortho images GCP Collection Coarse Alignment Fine Alignment Automatic • Uses improved model from Coarse Alignment • Searches for GCPs with lower error Tie Point Collection (Bundle Adjustment) Initial Alignment Automatic • Searches for Tie Points with lower error Page 38 Copyright © 2013 PCI Geomatics
- 39. QA Fine Alignment Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Fine QA Alignment GCPs/TPs Generate Ortho-Mosaic What does Fine Align QA Involve… Review GCPs and Tie Points Manual • Review distribution – More important after Fine Alignment If Required, Edit GCPs and Tie Points Manual • Delete blunder GCPs • Prune and/or add GCPs to areas with poor distribution Page 39 Copyright © 2013 PCI Geomatics
- 40. Orthorectification Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Fine Alignment Mosaic Ortho Mosaic Generate Generation Preparation Ortho-Mosaic High speed orthorectification Page 40 Copyright © 2013 PCI Geomatics
- 41. Mosaic Preparation Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Ortho Generation Fine Alignment Generate Ortho-Mosaic Mosaic Preparation Mosaic Generation 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 Page 41 Copyright © 2013 PCI Geomatics
- 42. QA Cutlines and Color Balancing Manual Semi-Auto Data Prep Data Ingest Automatic Coarse Alignment Ortho Generation Fine Alignment Generate Ortho-Mosaic Mosaic QA Cutlines & Color Preparation Mosaic Generation 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 Page 42 Copyright © 2013 PCI Geomatics
- 43. Mosaic Generation Manual Semi-Auto Automatic Data Prep Data Ingest Coarse Alignment Ortho Generation Fine Alignment Generate Ortho-Mosaic Mosaic QA Cutlines & Color Preparation Mosaic Generation Generate accurate seamless mosaics with OrthoEngine or GXL Page 43 Copyright © 2013 PCI Geomatics
- 45. 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 Page 45 Copyright © 2013 PCI Geomatics
- 46. Find out more about HAP Website Historical Airphoto Processing Website PDF Whitepaper YouTube PCI Tech TV HAP Commercial Interview with Lead HAP developer 46 Copyright © 2013 PCI Geomatics
- 47. Contact PCI Geomatics www.pcigeomatics.com info@pcigeomatics.com Page 47 Private and Confidential
Editor's Notes
- Notes:The HAP System has successfully processed projects containing imagery with an initial positional error of greater than 2000 pixels.Orientation accuracy is another important concept, which is related to the initial positional accuracy of images in a flight line.
- Notes:Land cover change can be a big issue with historical imagery. The ability to automatically collect GCPs from a reference image can be hindered by a large degree of land cover change between the reference image and the raw data. However, there are many different techniques that can be applied to overcome this problem, such as copying GCPs successfully collected on overlapping areas of neighboring images or by using the HAP System to create a new reference image of a date closer to that of the raw imagery. For example, if you are orthorectifying many different datasets of a given area over different years, correct imagery closer to the year of the reference image and then use that output mosaic as a reference to correct datasets from prior years.
- Notes:Land cover change can be a big issue with historical imagery. The ability to automatically collect GCPs from a reference image can be hindered by a large degree of land cover change between the reference image and the raw data. However, there are many different techniques that can be applied to overcome this problem, such as copying GCPs successfully collected on overlapping areas of neighboring images or by using the HAP System to create a new reference image of a date closer to that of the raw imagery. For example, if you are orthorectifying many different datasets of a given area over different years, correct imagery closer to the year of the reference image and then use that output mosaic as a reference to correct datasets from prior years.
- Notes:Scratches and damages on the imagery can hinder automation when collecting GCPs, Tie Points and fiducial marks. Scratches often appear as white or relatively bright grey marks that can confuse the correlators, making it more difficult to obtain accurate GCPs automatically.
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- Notes:The time it takes to complete a project is highly variable. Often it depends on the quality of the input imagery, which impacts the number of alignment iterations required and or manual effort. In general, most projects complete in about half the time it takes an operator to complete the same project using the traditional manual approach when attempting to achieve the same accuracy.
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- Notes:The optimal configuration of the HAP system is generally 2 QA/QC Machines for 1 Processing system. However, the configuration is determined on a customer-by-customer basis.The Processing System performs all of the automated process, which can be launched from a remote location (i.e. remote desktop). The QA/QC machines are installed with a special QA build of Geomatica and the QA/QC operations are performed on the machines, while accessing the data remotely over the network (data always stays on the processing System).
- Notes:Data Preparation is an automated step, but can often be optimized with scripts that setup the required inputs. PCI can customize scripts for customers, based on how their data is stored
- Notes:Data Preparation is quite simple, but depending on what information is available for the imagery, it can be time consuming. Data Preparation requires:Copying all of the raw input imagery into the same folder Input imagery must have the same fiducial types (i.e. corner or edge or corner and edge) Input imagery must have a focal length that is almost the sameInput imagery should be continuous (no major gaps or missing flight linesEnsuring you have a suitable reference image and DEM copied to the Reference folderCreating a Metadata file that containsApproximate center Longitude and Latitude coordinates Focal LengthInput Image nameApproximate flying Altitude (Feet above MSL)Physical Image DimensionsPCI can create scripts to help optimize the creation of the metadata file
- Notes:Data Ingest is the process of reading in the input information and outputting the imagery in working formats (PIX). Linked PIX files are generated to minimize the I/O time when ingesting data. Data ingest also consists of fiducial mark collection, which can often be automated.
- Notes:The Data Ingest stage also creates nominal (initial) georeferencing for the images, based on the input information.
- Notes:The purpose of the coarse alignment step is to improve the initial positional accuracy of the imagery so that it is suitable for the fine alignment step.Some more difficult projects may benefit from running the coarse alignment step multiple times, which iteratively improves the quality until it is suitable for the fine alignment.
- Notes:The coarse alignment step attempts to collect hundreds of GCPs and Tie Points for each image. This number of GCPs and Tie Points can effectively compensate for poor internal orientation due to limited information (i.e. missing radial distortion coefficients).The output of the coarse alignment step is an improved georeferenced image, which provides a better starting point for the fine alignment step.
- Notes:Quality Assurance is an important step. The level of manual edits required depends on the success of the automated coarse alignment run, which is often related to the quality of the input imagery.QA/QC of the coarse alignment step often requires removing bad GCPs and tie points and collecting GCPs on images that do not have any or that are not tied to a subset of imagery with GCPs.
- Notes:The fine alignment step uses the math model produced by the coarse alignment step as a starting point so that it has a better chance of successfully collecting GCPs and tie points on the images with the accuracy required to produce ortho images.It should never be required to run multiple fine alignment steps (all necessary improvements should be applied during the coarse alignment steps)
- Notes:QA of the fine alignment step should be limited to deleting bad GCPs and tie points to obtain the final desired accuracy. However, it is sometimes necessary to manually collect GCPs in images with poor distribution. For example, if an image has most of its GCPs in the top half of the image, it may be necessary to remove some GCPs from the top (called pruning) and manually add some GCPs to the bottom half.
- Notes:Orthorectification is a high speed automated process that can be performed using Geomatica or GXL
- Notes:The Mosaic Preparation step automatically generates cutlines and color balances the imagery. The output is a preview mosaic with cutlines and topology polygons for the images.
- Notes:Users can use PCI’s Mosaic Tool to manually edit cutlines and color balancing in order to ensure that the output is a seamless mosaic. PCI’s Mosaic Tool allows multiple people to connect to a single mosaic at the same time from different computers. A user can ‘check out’ a region of the mosaic to work on, which locks other users out of that area until it is checked back in.
- Notes:The final mosaic is a full resolution mosaic image produced using the cutlines and color balancing levels accepted in the QA/QC of the preview mosaic.