An Approach for UAV-based DSM Filtering Using Image ... · Filtering Using Image Classification and...
Transcript of An Approach for UAV-based DSM Filtering Using Image ... · Filtering Using Image Classification and...
![Page 1: An Approach for UAV-based DSM Filtering Using Image ... · Filtering Using Image Classification and Geometric Constraints Dr Mehdi Ravanbakhsh Remote Sensing Consultant at Astron](https://reader030.fdocuments.us/reader030/viewer/2022040621/5f3768e83b740b2b58290b48/html5/thumbnails/1.jpg)
An Approach for UAV-based DSM Filtering Using Image Classification
and Geometric Constraints
Dr Mehdi Ravanbakhsh Remote Sensing Consultant at Astron & Senior Research Fellow at
RMIT
email: [email protected]
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• Easy and fast deployment
• Low cost of operation for small sites
• Less prone to atmospheric effects
• Ultra-high resolution of up to 2 cm
• Good quality orthophotos and Digital Surface Model
• Ideal for mapping the natural environment
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Photogrammetric Processing Workflow
Multi-view Images
Geometric Alignment
Image Matching
Bundle Block
Adjustment
3D Point Cloud &
Orthophoto Generation
Automatic Aerial
Triangulation
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Spatial Product:Multi-spectral Orthoimagery
RBG overlaid on NIR mosaic at 4cm resolution
Image mosaic generated from 194 images
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Spatial Product: 3D Image
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UAV versus LiDAR
• High level of detail at pixel size density (1cm-4cm)
• No need for co-registration of LiDAR and Imagery
• Unlike LiDAR spectral information is available
• A fraction of LiDAR data aquisition cost
LiDAR UAV Point Cloud
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Accuracy Evaluation - Points outline Ground Control Points (GCPs): Red
Check Points (CKPs): Yellow
GCP No: 9
CKP No: 17
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Accuracy Evaluation - Points outline Ground Control Points (GCPs): Red
Check Points (CKPs): Yellow
GCP No: 9
CKP No: 17
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Accuracy Evaluation - Quantitative analysis
GCPs No 9
CKPs No 17
RMSE(∆E)(pl) 1.2
RMSE(∆N)(pl) 0.9
RMSE(∆H)(cm) 19
Image Resolution: 6cm pl: pixel E: Easting N: Northing
Random distribution of planimetric residuals
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DEM Generation - Point Cloud Processing
Above ground objects (gray) & Bare ground (brown)
• Major high differences indicate obove ground objects
• Window size should be greater than the size of the largest feature
• Average slope known
• Still misclassification exists
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DEM Generation - Image Classification RGB image mosaiec Clustring result Mask (red=1 & white=0)
• Unsupervised image clssification in 10 classes
• Merge classes to generate an image mask
• Point clouds on vegetation area are eleminated
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DEM Generation - Final Result RGB image mosaic DEM with geometry Image Mask DEM with combined
geometry & spectral
• Semi-automatic approach vs manual approach
• True DEM can be generated
• Other elevation products like Digital Canopy Model
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DCM Generation RGB Image DCM=DSM-DEM
Suitable dataset for tree stocking assessment and tree crown delineation
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Normalised DSM (nDSM) Generation
NIR Image
nDSM
Vegetation
Mask
Suitable dataset for building extraction
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Thank you & questions?