By Topic

An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xueliang Zhang ; Department of Geographic Information Science, Nanjing University, Nanjing, China ; Pengfeng Xiao ; Xuezhi Feng

Image segmentation is a critical step in the analysis of high-spatial-resolution remotely sensed imagery using object-based image analysis. The segmentation quality is extremely important to the subsequent analysis. This letter proposes an improved unsupervised method to evaluate the segmentation quality for remotely sensed imagery. The evaluation criteria take into account global intrasegment homogeneity and intersegment heterogeneity measures, which can be useful for the comparison of segmentation results produced by a single segmentation method. The proposed method is compared with other two mature unsupervised evaluation methods on two segmentation methods: region growing and mean shift. QuickBird images are used for the comparative study. The effectiveness of the proposed method is validated through comparing with the supervised evaluation method Rand Index and visual analysis.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:9 ,  Issue: 2 )