Ashwin Yerasi (University of Colorado)

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Presentation transcript:

Ashwin Yerasi (University of Colorado) ASSESSING PAVED ROAD SURFACE CONDITION WITH HIGH-RESOLUTION SATELLITE IMAGERY William J. Emery (University of Colorado) Ashwin Yerasi (University of Colorado) Nathan Longbotham (DigitalGlobe) Fabio Pacifici (DigitalGlobe) IGARSS 2014

Outline Background Road Quality Assessment Road Asphalt Identification Final Remarks IGARSS 2014

Background IGARSS 2014

Motivation In situ surveillance of paved road surfaces Primarily performed manually Slow and tedious Limited coverage Remote sensing of paved road surfaces Primarily performed automatically Comparatively efficient Large area coverage Latter technique can be used as a precursor or compliment to the former IGARSS 2014

Road Quality Surveillance Van (Pathway Services Inc.) In Situ Data Standard road surface parameters of interest Roughness (IRI) Rutting Cracking (fatigue, etc.) Interpretation of measurements varies by planning organization In general, road condition is rated holistically Good, fair, poor High, moderate, low drivability Etc. Road Quality Surveillance Van (Pathway Services Inc.) IGARSS 2014

WorldView-2 (DigitalGlobe) Remotely Sensed Data Provided by DigitalGlobe Collected by WorldView-2 spacecraft Panchromatic imagery 1 band (450-800 nm) Spatial resolution ~0.5 m 11-bit digital numbers Multispectral imagery 8 bands (400-1040 nm) Spatial resolution ~2 m WorldView-2 (DigitalGlobe) IGARSS 2014

Road Quality Assessment IGARSS 2014

Asphalt Degradation Lighter and less uniform appearance correlated with degradation Road quality thus potentially assessable through texture analysis of imagery Asphalt Spectra (M. Herold) Good Road (Boulder County) Fair Road (Boulder County) Poor Road (Boulder County) IGARSS 2014

Road Quality Assessment Overview Panchromatic Imagery Texture Filtered Imagery (3 Features) Asphalt Pixel Statistics Road Quality Road Asphalt ROIs IGARSS 2014

Digital Number 21B 115A 24A Colorado Springs Highways IGARSS 2014

Digital Number Good Fair Poor Mean 214.3 307.7 377.7 STD 5.2 10.3 29.5 IGARSS 2014

Data Range 21B 115A 24A Colorado Springs Highways IGARSS 2014

Data Range Good Fair Poor Mean 13.3 16.1 38.7 STD 3.5 6.2 15.6 IGARSS 2014

Variance 21B 115A 24A Colorado Springs Highways IGARSS 2014

Variance Good Fair Poor Mean 18.7 31.5 174.0 STD 10.7 27.6 145.7 IGARSS 2014

Entropy 21B 115A 24A Colorado Springs Highways IGARSS 2014

Entropy Good Fair Poor Mean 1.9 2.0 2.1 STD 0.2 0.1 IGARSS 2014

WorldView-2 Sensor Noise Analysis 𝜎 2 =𝑎+𝑏∙ DN 𝑎=2 𝑏=0.0143 WV-2 Sensor Noise (DigitalGlobe) Good Fair Poor Data DN Mean 214.3 307.7 377.7 Data DN STD 5.2 10.3 29.5 Sensor Noise 2.3 2.5 2.7 Colorado Springs Highways IGARSS 2014

Road Asphalt Identification IGARSS 2014

Road Asphalt Identification Overview OpenStreetMap Shapefiles Multispectral Imagery Pansharpened Imagery (8 Bands) Road Asphalt ROIs Panchromatic Imagery Texture Filtered Imagery (3 Features) IGARSS 2014

Original Scene with OSM Shapefile Road Identification Must disregard non-road features in scenery Use OpenStreetMap shapefiles as mask Original Scene with OSM Shapefile Masked Scene Original Scene IGARSS 2014

Asphalt Identification Must distinguish asphalt from non-asphalt features in roads Vehicles, paint, shadows, etc. 11 total dimensions contained in image pixels 8 spectral, 3 texture Training set manually selected Asphalt vs. non-asphalt Random forest classification Cohen’s kappa coefficient of ~0.91 obtained from experimental trials Asphalt Vehicle Paint Shadow IGARSS 2014

Final Remarks IGARSS 2014

Conclusions Road asphalt can be identified from high-resolution satellite imagery For the data analyzed, road asphalt becomes lighter in panchromatic grayscale shade as it degrades Digital number increases For the data analyzed, road asphalt becomes less uniform in texture as it degrades Data range increases Variance increases Entropy increases These apparent qualities can potentially be used to assess road pavement condition via satellite remote sensing IGARSS 2014

Questions IGARSS 2014

Backup Slides IGARSS 2014

Pansharpening Multispectral Panchromatic Pansharpened IGARSS 2014

Occurrence-Based Texture Filtering Original Image Filtered Image IGARSS 2014

Occurrence-Based Texture Filtering Digital Number (Original Data) Data Range Variance Entropy IGARSS 2014

Digital Number 25A 392A 287C Loveland Highways Loveland Highways IGARSS 2014

Digital Number Good Fair Poor Mean 191.7 239.5 300.5 STD 4.2 5.6 14.9 IGARSS 2014

Data Range 25A 392A 287C Loveland Highways Loveland Highways IGARSS 2014

Data Range Good Fair Poor Mean 7.3 12.8 17.0 STD 2.8 7.8 8.6 IGARSS 2014

Variance 25A 392A 287C Loveland Highways Loveland Highways IGARSS 2014

Variance Good Fair Poor Mean 6.4 31.9 48.6 STD 5.2 52.3 48.5 IGARSS 2014

Entropy 25A 392A 287C Loveland Highways Loveland Highways IGARSS 2014

Entropy Good Fair Poor Mean 1.7 1.8 1.9 STD 0.2 IGARSS 2014

WorldView-2 Sensor Noise Analysis 𝜎 2 =𝑎+𝑏∙ DN 𝑎=2 𝑏=0.0143 WV-2 Sensor Noise (DigitalGlobe) Good Fair Poor Data DN Mean 191.7 239.5 300.5 Data DN STD 4.2 5.6 14.9 Sensor Noise 2.2 2.3 2.5 Loveland Highways IGARSS 2014