Abstract
In this chapter, we provide two texture based change detection methods. In both methods, we calculate the texture descriptors for bitemporal images separately. In order to detect possible changes, we find their difference. We start with gray level co-occurrence matrix based texture descriptors next.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)
Tomowski, D., Klonus, S., Ehlers, M., Michel, U., Reinartz, P.: Change visualization through a texture-based analysis approach for disaster applications. In: Proceedings of ISPRS Technical Commission VII Symposium-100 Years ISPRS Advancing Remote Sensing Science Sensing, vol. 38, pp. 263–268 (2010).
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Education, (2004)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Cem Ünsalan
About this chapter
Cite this chapter
İlsever, M., Ünsalan, C. (2012). TEXTURE ANALYSIS BASED CHANGE DETECTION METHODS. In: Two-Dimensional Change Detection Methods. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-4255-3_4
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4255-3_4
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4254-6
Online ISBN: 978-1-4471-4255-3
eBook Packages: Computer ScienceComputer Science (R0)