By Topic

Multiresolution Remote Sensing Image Clustering

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

4 Author(s)
Wemmert, C. ; Univ. of Strasbourg, Strasbourg ; Puissant, A. ; Forestier, G. ; Gancarski, P.

With the multiplication of satellite images with complementary spatial and spectral resolution, a major issue in the classification process is the simultaneous use of several images. In this context, the objective of this letter is to propose a new method which uses information contained in both spatial resolutions. The main idea is that on one hand, the semantic level associated with an image depends on its spatial resolution, and on the other hand, information given by these images is complementary. The goal of this multiresolution image method is to automatically build a classification using knowledge extracted from both images, by unsupervised way and without preprocessing image fusion. The method is tested by using a Quickbird (2.8 m) and a SPOT-4 (20 m) image on the urban area of Strasbourg (France). The experiments have shown that the results are better than a classical unsupervised classification on each image and comparable to a supervised region-based classification on the high-spatial-resolution image.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 3 )