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

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

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. 

Abstract

Outlines

GIS Data of Roads

Characteristics of Roads

Problems in Extracting Roads from Imagery

Road Detection Methods

Road Tracking Methods

Trends

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

GIS data of Roads

GDTGeographic Data Technology, Inc.

Enhanced TIGER DB

Using DOQ and satellite imagery to update their spatial DB

GIS data of Roads

NAVTECHNavigation Technology, Inc.

Using existing maps

Digitizing based on aerial photographs

Driving and testing with GPS

Characteristics of RoadsRadiometric

Various grayscale along road extent Relatively constant grayscale and texture between

boundaries

Spectral Consistent signature Spectral response depends on material

Characteristics of RoadsGeometric

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

Problems in Extracting Roads from ImageryRadiometric

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

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.

Problems in Extracting Roads from Imagery

Road Detection Methods

Using radiometric information

Using geometric information

Using LIDAR data

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.

Using radiometric information

MethodsConvolution

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.

Using radiometric informationMethods

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

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

Using geometric informationMethods

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

Using LIDAR dataBerg. 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

Using LIDAR dataPhotogrammetric 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

Road Tracking Methods

Hough Transform

Optimal Search

Profile Analysis

Hough TransformComputing global description of features with given measurements.Determine both WHAT the features are and HOW MANY of them exist.Parametric description of a line

x

y

r

xcos + ysin = r

(x, y) -> (r, )

Hough transformProcedures

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.

Hough transformAdvantages

Tolerant of gaps in feature boundary.

Unaffected by image noise.

DisadvantagesDistance between points on lines is not considered.

Optimal searchDirectional cone search (Lee et al. 1999)

Represent local trend of featuresSearching 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.

Optimal searchDirectional cone search

Useful when roads are defined as long rectangular objects.

Tracking result is good in urban area.

Affected by image noises.

Profile analysisGDPA (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.

…

a1 a2 a3

a8 g a4

a7 a6 a5

A4

A1

A2A3

Profile analysisGDPA

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.

TrendsStrategies

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

TrendsStrategies

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

Possible operators for road detection

 

Phases Information OperatorsRoad region finding Radiometric/Spectral Edge detection filter

Canny filter

Watershed transform

Wavelet transform

(multiresolution approach)

Road shaping Radiometric/Spatial Parallel edge detection filter

Road templates / LIDAR

Road linking Spatial only Hough transform(global)

Optimal search(local)

Direction filter(local)

Overlaying with GIS layer

LIDAR

Thinning / vectorizing

Attributes Self-Organizing Map (SOM)

Snakes, Template Matching

Dynamic Programming

Trend

Road Region Finding

Road Linking

Road Shaping

Thinning / Vectorizing

Canny Filter

Parallel Edge Filter

Road Network

Input Images

GIS Layer (Optional)

Hough / Optimal

SOM, Snakes

LIDAR

SAR ImageryA SAR SPECKLE FILTERING ALGORITHM TOWARDS

EDGE SHARPENING

Yunhan Dong*, Anthony K Milne**, and Bruce C Forster*

*School of Geomatic Engineering, **Office of Postgraduate Studies

The University of New South Wales

Sydney 2052, Australia

y.dong@unsw.edu.au, t.milne@unsw.edu.au, b.forster@unsw.edu.au

Working Group VII/6

Filters Applied on Non-Edge Features

Filters Applied on Edge Features

GIS Assisted Feature ExtractionMATCHING LINEAR FEATURES FROM SATELLITE IMAGES WITH SMALL-SCALE GIS DATA

Reference:

Andreas BUSCHBundesamt fur Kartographie und GeodasieRichard-Strauss-Allee 1160598 Frankfurt am Main, Germanybusch@ifag.de

GIS-Image Analysis Flow

Flow of information between GIS and image analysis.

Image

Analysis

GIS

Prior InformationRevision

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:

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

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.

METHODOLOGYSplit 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.

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.

SYSTEM TRENDS FOR AUTOMATED FEATURE EXTRACTION

DIGITAL SYSTEMS FOR AUTOMATED CARTOGRAPHIC FEATURE EXTRACTION

Reference

Eberhard Gü lchUniversity of Bonn, GermanyInstitute of Photogrammetryebs@ipb.uni-bonn.de

1. Interactive system (purely manual measurement, no automation for any measurement task)

2. Semi-automatic system (interactive environment and integration of automatic modules in the workflow)

3. Automated system (interactive environment with interaction before and after the automatic phase)

4. Autonomous system.

SYSTEM TRENDS FOR AUTOMATED FEATURE EXTRACTION

Commercially available photogrammetric systems now include feature collection module

In last year´ s comprehensive evaluation by GIM International (Plugers, 1999), there are 19 digital photogrammetric workstations listed

The basic input are digital or digitized images with the emphasis on stereo-imagery.

SYSTEM TRENDS FOR AUTOMATED FEATURE EXTRACTION

Three types of methods are distinguished in the GIM survey:

• Semi-automatic line extraction (7 systems)

• Semi-automatic corner point extraction (5 systems)

• Automatic break-line extraction (3 systems)

SYSTEM TRENDS FOR AUTOMATED FEATURE EXTRACTION

SCALE-SPACE EXTRACTION TECHNIQUES

MULTI-SCALE ROAD EXTRACTION USING LOCAL AND GLOBAL GROUPING CRITERIA

Reference

Albert Baumgartner, Stefan Hinz

Chair for Photogrammetry and Remote SensingTechnische Universit¨ at M¨ unchen, D–80290 Munich, GermanyE-mail: falbertgfhinzg@photo.verm.tu-muenchen.deURL: http://www.photo.verm.tu-muenchen.de

(a) Image (b) Segmentation of open rural context

(a) Initial hypotheses for road segments (b) Detail

Results of local grouping.Results of global grouping.

Results of sequential combination of local and global grouping.

Results of integrated combination of local and global module