Comprehensive Survey of Extraction Techniques of Linear Features from Remote Sensing Imagery for Updating Road Spatial D

1. Ji Sang Park, PhD Candidate Dr. Raad A. Saleh, Scientist Comprehensive Survey of Extraction Techniques of Linear Features from Remote Sensing Imagery for Updating Road Spatial Databases Department of Civil and Environmental Engineering University of Wisconsin-Madison ERSC 12th Floor 1225 West Dayton Street, Madison, WI 53706 Phone: 608-263-3622 Fax: 608-262-5964

2. Comprehensive Survey of Extraction Techniques of Linear Features from Remote Sensing Imagery for Updating Road Spatial Databases Abstract Research on automated and semi-automated extraction techniques of linear features from remote sensing imagery has been active for decades. Features of interest include transportation networks, power transmission lines, etc.  This paper presents a comprehensive survey of extraction techniques of such features from aerial and satellite imagery.  The techniques are evaluated with respect to methodology, strengths, drawbacks, and implementation approach.  Source data for the surveyed techniques include panchromatic and multispectral imagery.  The viability of hyperspectral data is extrapolated for same purpose of utilization. The paper later presents a discussion of automated extraction techniques specifically used for updating road spatial databases. 

3. Outlines • GIS Data of Roads • Characteristics of Roads • Problems in Extracting Roads from Imagery • Road Detection Methods • Road Tracking Methods • Trends

4. GIS data of Roads • National Highway Planning Network • BTS data • Federal Highway Administration • Scale : 1:100,000 • Representing 400,000 miles of federal roads in 50 states including Puerto Rico • DB updating method varies

6. GIS data of Roads • GDT • Geographic Data Technology, Inc. • Enhanced TIGER DB • Using DOQ and satellite imagery to update their spatial DB

8. GIS data of Roads • NAVTECH • Navigation Technology, Inc. • Using existing maps • Digitizing based on aerial photographs • Driving and testing with GPS

10. Characteristics of Roads • Radiometric • Various grayscale along road extent • Relatively constant grayscale and texture between boundaries • Spectral • Consistent signature • Spectral response depends on material

11. Characteristics of Roads • Geometric • Long and continuous • Narrow width • Two-lane : 4.8m(16ft) ~ 7.2m(24ft) with 3m shoulders • Divided : 3.6m(12ft) travel lane with 6m(20ft) wide median strip • With small curvatures • Different shapes • High-resolution: Rectangular objects with parallel boundaries • Low-resolution: Linear objects

14. Problems in Extracting Roads from Imagery • Radiometric • Line disconnection due to covering over roads • Trees, shadows, buildings, and vehicles • Detection of wrong objects or areas due to similar grayscale • Objects or surrounding of roads • Blurred boundaries of roads • Spectral • Different spectral information due to camera angle, atmospheric distortions, etc. • Inconsistent spectral response • Inaccurate signatures

15. Problems in Extracting Roads from Imagery • Geometric • Different horizontal profiles due to various widths and types of roads • Number of lanes • Divided / undivided • Short or dead-end road • Note • Important to keep balance between detection performance and local condition. • The more edges are extracted, the more complex they become.

16. Road Detection Methods • Using radiometric information • Using geometric information • Using LIDAR data

17. Using radiometric information • Convolution or image segmentation. • Popular method for approximating initial road regions. • Amount of data is reduced significantly while retaining structural information of features of interest. • Most of the methods adopt Gaussian smoothing to reduce small details.

18. Using radiometric information • Methods • Convolution • High pass filter : detect high frequency • Canny filter : global position of tracked discontinuities • Nevatia-Babu filter : edge detection + edge thinning • Gradient Direction Profile Analysis (GDPA) • Determine gradient direction for a pixel as the direction of maximum slope. • Image segmentation • Watershed transform • Partitions an image into homogeneous regions. • Locates gradient contours based on the gradient magnitude and direction. • Assisted by multiscale image analysis • Indicate global location and relative size of terrain objects.

19. Using radiometric information • Methods • Signal processing • “a trous” algorithm • Multiresolution analysis (MRA) • Eliminate small particles by smoothing • Describe the hierarchical information of features. • Wavelet transform • Establish a local relationship between a spatial domain and a frequency domain. • Approximate the first derivatives of the pixel. • Computation of successive approximations by smoothing. • Determine edges based on wavelet coefficients. • Neural Network • Dynamic programming • Defining a cost which depends on local information • Summation – minimization process

20. Using radiometric information • Convolve the image in the spatial domain using an appropriate kernel • Kernels can be used for connecting segments • Connected components are labeled

21. Using geometric information • Methods • Convolution • Direction filter : direction of extracted regions • Parallel edge detection filter : parallelism of edges • Optimal search algorithm • Distances and directions between road segments • Hough transform • Connectivity of line segments can be computed analytically • Tolerant of gaps in feature boundary descriptions • Using templates and models

22. Using LIDAR data • Berg. R. and Ferguson, J. (2000) • Classification was primarily for removal of vegetation data • Where applicable, building data were also removed • Possible for road shaping and line linking • Rigorous manual analysis and edit was required

23. Using LIDAR data • Photogrammetric mapping provides a better representation of narrow features since accurate breakline data points can be collected directly along the feature of interest • Not effective for feature mapping • The raw data points may not be located directly on the features. • Does not define breaklines along features. • Advantages • Density of points • Ability to penetrate canopy • Effective for large project area within short time period

24. Road Tracking Methods • Hough Transform • Optimal Search • Profile Analysis

25. Hough Transform • Computing global description of features with given measurements. • Determine both WHAT the features are and HOW MANY of them exist. • Parametric description of a line y xcos + ysin = r (x, y) -> (r,  ) r  x

26. Hough transform • Procedures • Points in cartesian image space map to curves in the polar Hough parameter space. • Curves by collinear points intersect in peaks (r,  ). • Intersection points characterize the straight line segments. • Extract local maxima from the accumulator array (relative threshold). • Mapping back from Hough space into image space.

27. Hough transform • Advantages • Tolerant of gaps in feature boundary. • Unaffected by image noise. • Disadvantages • Distance between points on lines is not considered.

28. Optimal search • Directional cone search (Lee et al. 1999) • Represent local trend of features • Searching process • Shoot two cones with the direction of the region. • The cones may meet several regions. • Choose the most probable road region and connect two regions by adding regions between two regions. • Repeat from the beginning until no more reconnection occurs.

29. Optimal search • Directional cone search • Useful when roads are defined as long rectangular objects. • Tracking result is good in urban area. • Affected by image noises.

30. Profile analysis • GDPA (Gradient Direction Profile Analysis) • Gradient direction: direction of max slope among four defined directions near a pixel • A1 = [|a4 – g| + |a8 – g|] / 2 • Perpendicular to the ridge for the pixel • Highest point correspond to the top of ridge. • Additional fitting function is used between steep slopes and gentle slopes. … A3 A2 A4 A1

31. Profile analysis • GDPA • Advantages • Edge detection & road tracking are done simultaneously. • Describe local conditions of features. • Simple procedure using only gradient value. • Disadvantages • Similar radiometric contrast between roads and surroundings provides bad result. • By using small size convolution window, tracking effect is not good in urban area due to complex structures and various obstacles.

32. Trends • Strategies • Using both radiometric and geometric information • Radiometric: find road regions in images • Geometric: construct parallel boundaries and link disconnected road segments • Image resolution • High-resolution : matching profile and detection of road sides • Low-resolution : detection and following of lines

33. Trends • Strategies • Exploiting GIS layer • Can be used for road linking, but not for road positioning • Using LIDAR data • Can be used for road shaping and linking as a reference data

34. Possible operators for road detection

35. Trend Input Images Road Region Finding Canny Filter Parallel Edge Filter Road Shaping LIDAR Hough / Optimal Road Linking GIS Layer (Optional) Thinning / Vectorizing SOM, Snakes Road Network

36. SAR Imagery • A SAR SPECKLE FILTERING ALGORITHM TOWARDS EDGE SHARPENINGYunhan Dong*, Anthony K Milne**, and Bruce C Forster**School of Geomatic Engineering, **Office of Postgraduate StudiesThe University of New South WalesSydney 2052, Australiay.dong@unsw.edu.au, t.milne@unsw.edu.au, b.forster@unsw.edu.auWorking Group VII/6

37. Filters Applied on Non-Edge Features

38. Filters Applied on Edge Features

39. GIS Assisted Feature Extraction • MATCHING LINEAR FEATURES FROM SATELLITE IMAGES WITH SMALL-SCALE GIS DATA • Reference: • Andreas BUSCH • Bundesamt fur Kartographie und Geodasie • Richard-Strauss-Allee 11 • 60598 Frankfurt am Main, Germany • busch@ifag.de

40. GIS-Image Analysis Flow GIS Prior Information Revision Image Analysis Flow of information between GIS and image analysis.

41. Measures and Criteria for Matching • All possible correspondences within the neighborhood defined by a maximal distance; there is need for measures to evaluate the quality of different matches. • Distance: • Length: • Parallelism: • Semantics:

44. INTEGRATED GEOGRAPHIC INFORMATION SYSTEMS – IMAGE ANALYSIS INTEGRATED GEOGRAPHIC INFORMATION SYSTEMS: FROM DATA INTEGRATION TO INTEGRATED ANALYSIS Reference: Manfred EHLERSInstitute for Environmental SciencesUniversity of VechtaP.O. Box 1553, D-49364 Vechta, Germanymehlers@ispa.uni-vechta.de

46. CARTOGRAPHIC FEATURES FROM AERIAL IMAGES AUTOMATIC CARTOGRAPHY FROM AERIAL IMAGES (SITE OF SALE’, MOROCCO) Reference: O.El Kharki*,M.Sadgal*,A.Ait Ouahman*,A.El Himdy**,M.Ait Belaid****Laboratory of Electronic and Instrumentation,Faculty of Science Se lalia,BOX 2390 Marrakech,Morocco.elkharki@yahoo.fr**Ad inistration de la Conservation Foncière du Cadastre et de la Cartographie,Rabat,Morocco.***Royal Centre for Remote Sensing,Rabat,Morocco.

48. METHODOLOGY • Split and Merge Algorithm • The basic idea of region splitting is to break the image into a set of disjoint regions which are coherent within the selves: • -Initially take the image as a whole to be the area of interest. • -Look at the area of interest and decide if all pixels contained in the region satisfy some similarity constraint. • -If TRUE then the area of interest corresponds to a region in the image. • -If FALSE split the area of interest (usually into four equal sub-areas)and consider each of sub-areas as the area of interest in turn. • -This process continues until no further splitting occurs.In the worst case this happens when the areas are just one pixel in size.

49. METHODOLOGY • If only a splitting schedule is used then the final segmentation would probably contain any neighboring regions that have identical or similar properties. • Thus,a merging process is used after each split which co pares adjacent regions and merges the if necessary.