Download presentation
Presentation is loading. Please wait.
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
π 2 =π+πβ DN π=2 π=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 Cohenβs 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
π 2 =π+πβ DN π=2 π=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
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.