Road Extraction and Feature Conflation

Transcription

1 Road Extraction and Feature Conflation Timothy L. Haithcoat & Wenbo Song University of Missouri Department of Geography Director - Geographic Resources Center Director - Missouri Spatial Data Information Service Deputy Director - Center for Geospatial Intelligence HaithcoatT@missouri.edu 1 Road Profile Bar-shaped shaped Parabolically 2 1

2 Accuracy Assessment Completeness = Length of matched extraction Length of reference Correctness = Length of matched extraction Length of extraction Quality = Length Length of matched extraction of extraction + Length of unmatched reference RMSE = 2 2 [( xextrcted xreference ) + ( yextracted yreference ) ]/ n Horizontal Accuracy = * RMSE (Root Mean Square Error) 3 4 2

3 5 Correct Uncorrect Unextracted 6 3

4 Accuracy Assessment for Rural DOQ Matched Unmatched Total Length (m) Buffer Distance (m) Completeness Correctness Quality RMSE (m) Accuracy (m) Extracted Road Reference & % 77% 71%

5 9 Correct Uncorrect Unextracted 10 5

6 Accuracy Assessment for IKONOS Matched Unmatched Total Length (m) Buffer Distance (m) Completeness Correctness Quality RMSE (m) Accuracy (m) Extracted Road % 66% 58% Reference Automated Feature Extraction Issues: - Automated feature extractions often require a lot of downstream human intervention to edit and clean up. - Not many attributes can be directly extracted from imagery. Traditional Conflation Approach Conflation is the process that combines two spatial datasets of the same region to produce a superior dataset that is better than either source dataset in spatial and attribute aspects. Processes: Feature matching Map alignment (rubber sheeting) Attribute transfer 12 6

7 Vector Migration Approach 13 Vector Migration Approach 14 7

8 The Result of Vector Migration 15 Vector Migration Rubber sheeting works for straight lines, but problems exist for curves The accuracy of results depend on original data and the density, distribution of control points 16 8

9 The TIGER Centerline 17 Result of Vector Migration 18 9

10 Snake The Snake is an active contour model under the influence of internal and external forces. The internal force imposes a piecewise smoothness constraint. The external image force pushes the snake toward salient image features such as lines and edges. Snakes have been used extensively in many image processing and computer vision applications m The problem of non-systematic spatial displacement of TIGER road vector 115 m 75 m 78 m 85 m 20 10

11 The road intersections and road terminations from vector data 21 Automatically extracted road intersections and road terminations from imagery 22 11

12 Automatic point matching between vector and imagery 23 Results of piecewise transformation (rubbersheeting). Some roads still need correction 10 m 45 m 24 12

13 Improvement by snake 25 Improvement by snake 26 13

14 Improvement by snake 27 Improvement by snake 28 14

15 Improvement by snake 29 Move the vertices to road center 30 15

16 Move the vertices to road center 31 Final results by generalization 32 16

17 Thank You Questions or Comments Timothy L. Haithcoat 104 Stewart Hall Univ. of Missouri Columbia, MO Phone: