Presentation is loading. Please wait.

Presentation is loading. Please wait.

Ashwin Yerasi (University of Colorado)

Similar presentations


Presentation on theme: "Ashwin Yerasi (University of Colorado)"— Presentation transcript:

1 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

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

3 Background IGARSS 2014

4 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

5 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

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

7 Road Quality Assessment
IGARSS 2014

8 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

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

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

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

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

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

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

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

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

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

18 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

19 Road Asphalt Identification
IGARSS 2014

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

21 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

22 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

23 Final Remarks IGARSS 2014

24 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

25 Questions IGARSS 2014

26 Backup Slides IGARSS 2014

27 Pansharpening Multispectral Panchromatic Pansharpened IGARSS 2014

28 Occurrence-Based Texture Filtering
Original Image Filtered Image IGARSS 2014

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

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

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

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

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

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

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

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

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

38 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


Download ppt "Ashwin Yerasi (University of Colorado)"

Similar presentations


Ads by Google