Share Email Print

Proceedings Paper

Unsupervised change detection by kernel clustering
Author(s): Michele Volpi; Devis Tuia; Gustavo Camps-Valls; Mikhail Kanevski
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents a novel unsupervised clustering scheme to find changes in two or more coregistered remote sensing images acquired at different times. This method is able to find nonlinear boundaries to the change detection problem by exploiting a kernel-based clustering algorithm. The kernel k-means algorithm is used in order to cluster the two groups of pixels belonging to the 'change' and 'no change' classes (binary mapping). In this paper, we provide an effective way to solve the two main challenges of such approaches: i) the initialization of the clustering scheme and ii) a way to estimate the kernel function hyperparameter(s) without an explicit training set. The former is solved by initializing the algorithm on the basis of the Spectral Change Vector (SCV) magnitude and the latter is optimized by minimizing a cost function inspired by the geometrical properties of the clustering algorithm. Experiments on VHR optimal imagery prove the consistency of the proposed approach.

Paper Details

Date Published: 22 October 2010
PDF: 8 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300V (22 October 2010); doi: 10.1117/12.864921
Show Author Affiliations
Michele Volpi, Univ. de Lausanne (Switzerland)
Devis Tuia, Univ. de Lausanne (Switzerland)
Univ. de València (Spain)
Gustavo Camps-Valls, Univ. de València (Spain)
Mikhail Kanevski, Univ. de Lausanne (Switzerland)

Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?