A Semi-Automatic Method for Detecting Changes to Ordnance Survey速 Topographic Data in Rural Environments C. Gladstone, A. Gardiner, D. Holland Ordnance Survey - Research
Introduction to Ordnance Survey® • Founded in 1791 • National Mapping Agency for Great Britain • Not just maps! • • • •
1200 employees Field Surveyors (300) Photogrammetry department (70) Research team (25)
Motivation • Updating the National Topographic Database is our main task
Urban – 1:1250 Rural – 1:2500
• 222,000km2 of ‘Rural’ and ‘Mountain and Moorland’ data • Capture 70,000 km2 of aerial imagery per year • Manual photogrammetric data capture – hugely labour intensive • Identifying change is a big task • Semi-automation = faster update
Mountain and Moorland – 1:10000
Proposed Change Detection Flowline 1. Manual search for change Current manual flowline
Image capture & pre-processing
and 2. Manual topographic map update
Proposed Semi-automatic flowline
Image capture & pre-processing
Automatically identify ‘Change Candidates’
Manual update using only ‘Change Candidates’
Transferable
• Works in all types of geographical areas • No training data or manual ruleset calibration Efficient and easy to use
• Operated by expert photogrammetrists, not GEOBIA experts • Uses commercially available software • Compatible with current photogrammetric flowline
Changes in Rural Areas • Focus on changes within rural areas • Features dictated by OS topographic data capture specification; • New/demolished/significantly changed buildings, • Roads, tracks and paths, • Field boundaries – hedges, walls, fences, • Vegetation – trees, scrub, grass/crops • Sealed and unsealed surfaces, • Inland water – rivers, lakes, ponds.
• Line and area features
Input Data
Vexcel UltraMap
BAE Systems SOCET SET
Nonpansharpened 4-Band Imagery
Panchromatic Imagery
4-band Orthomosaic (50cm GSD)
DSM
ESRI速 ArcGIST M 10
eCognition 速
nDSM
Classification and Change Detection
Land-Form PROFILE速 DTM
Topographic Data
General Approach •
Classification and change detection achieved in eCognition®
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Segmentation uses both spectral and height data Tend to oversegment - refined as classification progresses
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Classification is more reliant on height than spectral data nDSM used as a guide only. Relative DSM difference more reliable Normalized ratios of spectral bands used whenever possible…
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Change Detection is raster to vector (classification to pre-existing topographic data)
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A few more detailed examples…
Spectral Thresholds Set from Ortho-mosaic Image •
Preliminary Tree and Building classification
“Dark” segments neighbouring “tall” objects temporarily classified as shadow candidates Temporary shadow candidates used to calculate brightness thresholds for shadow class • Shadow classification used to help filter ‘Change Candidates’
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4-band Ortho-mosaic seamlines are used to constrain spectral threshold calculations
Detecting New Linear Features • Linear features can’t be readily classified… • Canny Edge layer automatically calculated within eCognition® • Edges filtered according to length and width to find significant lines • Multi-threshold segmentation used to generate significant line segments
• Line segments filtered and classified by context, shape and spectral criteria
• Intersect classified lines with topographic data to detect new linear features
Use of Existing Topographic Vector Data • •
Road edges in existing topographic data used to guide segmentation Known road segments used as initial candidates for ground surface in classification
• Existing topographic data for water used to calculate thresholds for water classification
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Existing topographic vector data for buildings used as comparison to test for demolished buildings
Aerial Image - May 2010
Digital Surface Model
Automatic Classification Result
Change Detection Result
Production Trial – Remote Sensing, Autumn 2012
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Two 3x3km trial areas in West Sussex “Typical” rural geography, containing a range of “typical” features
Results - Efficiency • Each trial area was updated twice • Once using the “traditional” manual method • Once using the automatic change detection method • Efficiency savings when using automatic method = 25% - 35% Examples of changes identified:
New building and path
“New” area of trees
Sealed to unsealed surface
Results - Correctness • Correctness (% of auto predicted changes that were genuine): • Correctness = 35% - 45% • Acceptable because it is quick/easy to reject incorrect changes
Typical false alarms:
Crop boundaries as New Lines
Leaf-off trees as New Buildings
Small, low buildings as Demolished Buildings
Results - Completeness • Completeness (% of the real changes that were found): • Completeness = 79% • •
Only 1 high priority (category A) change missed on each site 404 genuine changes across both trial sites
Typical missed features:
Subtle, new short fence
Small, low new farm building
Part-demolished short fence
Next steps Move from research project to a production tool • Designing a robust, production strength flowline
Process improvements • Investigate use of pan-sharpened 4-band imagery • More detailed land cover classes (heath, rough grass, marsh)
Other uses for the classification • Input to potential land cover product • Classification used to filter DSM to DTM
Thank you for listening
Andy.Gardiner@ordnancesurvey.co.uk +44 (0)23 8005 5759 www.ordnancesurvey.co.uk
Ordnance Survey, Adanac Drive, Southampton, United Kingdom, SO16 0AS