Remote Sensing Applications and Decision Support

Adapted sparse fusion with constrained clustering for semisupervised change detection in remotely sensed images

[+] Author Affiliations
Anisha M. Lal, S. Margret Anouncia

VIT University, School of Computer Science and Engineering, Vellore, India

J. Appl. Remote Sens. 11(1), 016013 (Jan 27, 2017). doi:10.1117/1.JRS.11.016013
History: Received August 31, 2016; Accepted January 4, 2017
Text Size: A A A

Abstract.  Change detection is defined as the process that helps in determining the changes associated with land use and land cover properties with reference to georegistered multitemporal remote sensing data. A unique idea of integrating sparse fusion with constrained clustering has been attempted to detect changes in multitemporal and multispectral remote sensing images in a framework called ASCC (to be pronounced as “ask”). Unlike the other traditional methods of change detection, this approach focuses on the fusion of difference images followed by a semisupervised clustering to construct a change detection map in binary form. An enhanced sparse representation is adopted to improve the performance of the fusion process by creating a locally adaptive dictionary that extracts patches from the two difference images. The reconstruction of the fused image is performed using maximum absolute coefficients of the learned dictionary. The resultant fused image is subjected to the change detection process through constrained clustering to discern the changed pixels from the unchanged pixels. To evaluate the designed solution, the method is quantified through percentage correct classification and the process is compared with the existing methods of clustering such as k-means, adaptive k-means, fuzzy C-means, and enhanced constrained k-means. The simulation results demonstrated that the designed approach performed better when compared with state-of-art change detection methods for detecting changes in multitemporal images.

Figures in this Article
© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Anisha M. Lal and S. Margret Anouncia
"Adapted sparse fusion with constrained clustering for semisupervised change detection in remotely sensed images", J. Appl. Remote Sens. 11(1), 016013 (Jan 27, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.016013


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.