Urban major road extraction from IKONOS imagery based on modified texture progressing analysis technique

Xuewen Wu; Hanqiu Xu; Pingli Wu

doi:10.1117/12.872950

3 November 2010 Urban major road extraction from IKONOS imagery based on modified texture progressing analysis technique

Xuewen Wu, Hanqiu Xu, Pingli Wu

Author Affiliations +

Proceedings Volume 7840, Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality; 78401L (2010) https://doi.org/10.1117/12.872950
Event: The Sixth International Symposium on Digital Earth, 2009, Beijing, China

Abstract

A method for urban major road extraction from IKONOS imagery was proposed. The texture features of the image were first analyzed in three different levels. The first level calculated the Mahalanobis distance between test pixels and training pixels. The second level was the calculation results of Bhattacharyya distance between the distributions of the pixels in the training area and the pixels within a 3×3 window in the test area. The third level employed cooccurrence matrices over the texture cube built around one pixel, and then Bhattacharyya distance was used again. The processed results were thresholded and thinned, respectively. With the assistance of the geometrical characteristic of roads, the three resultant images corresponding to three levels were computed using fuzzy mathematics for their likelihood belonging to road and then merged together. A knowledge-based algorithm was used to link the segmented roads. The result was finally optimized by polynomial fitting. The experiment shows that the proposed method can effectively extract the urban major roads from the high-resolution imagery such as IKONOS.

Citation Download Citation

Xuewen Wu, Hanqiu Xu, and Pingli Wu "Urban major road extraction from IKONOS imagery based on modified texture progressing analysis technique", Proc. SPIE 7840, Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality, 78401L (3 November 2010); https://doi.org/10.1117/12.872950

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available