Research Papers

Optimization of multiresolution segmentation by using a genetic algorithm

[+] Author Affiliations
Maryam Nikfar

KNTU, Tehran, Iran

Mohammad Javad Valadan Zoej

KNTU, Tehran, Iran

Ali Mohammadzadeh

KNTU, Tehran, Iran

Mehdi Mokhtarzade

KNTU, Tehran, Iran

Afshin Navabi

Farand Co, Tehran, Iran

J. Appl. Remote Sens. 6(1), 063592 (Oct 30, 2012). doi:10.1117/1.JRS.6.063592
History: Received November 29, 2011; Revised September 5, 2012; Accepted September 17, 2012
Text Size: A A A

Abstract.  Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. Multiresolution segmentation, which was proposed by Baatz and Schäpe, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz’s multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz’s multiresolution segmentation approach for three building groups’ meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images.

Figures in this Article
© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Maryam Nikfar ; Mohammad Javad Valadan Zoej ; Ali Mohammadzadeh ; Mehdi Mokhtarzade and Afshin Navabi
"Optimization of multiresolution segmentation by using a genetic algorithm", J. Appl. Remote Sens. 6(1), 063592 (Oct 30, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063592


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.