By Topic

Statistical, Structural, Hybrid, and Graph Theoretical Features to Measure Land Development

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
$31 $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

1 Author(s)
Unsalan, C. ; Dept. of Electr. & Electron. Eng, Yeditepe Univ., Istanbul

Extracting information on a developing region from its sequential satellite images has many benefits. Therefore, in a previous study, we introduced graph theoretical and conditional statistical features to measure land development in a predefined region. There, we only used the grayscale information from the satellite image at hand. Here, we extend that work by introducing novel statistical, hybrid, and graph theoretical features using multispectral information. We also introduce novel structural features based on three different structure extraction methods. We test our new features on a diverse data set and report their performances in measuring land development.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 1 )