Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory

Antoine Lefebvre; Thomas Corpetti; Laurence Hubert Moy

doi:10.1117/12.864802

22 October 2010 Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory

Antoine Lefebvre, Thomas Corpetti, Laurence Hubert Moy

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300E (2010) https://doi.org/10.1117/12.864802
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

This paper is concerned with the segmentation of very high spatial resolution panchromatic images. We propose a method for unsupervised segmentation of remotely sensed images based on texture information and evidence theory. We first perform a segmentation of the image using a watershed on some coefficients issued from a wavelet decomposition of the initial image. This yields an over-segmented map where the similar objects, from a textural point of view, are aggregated together in a step forward. The information of texture is obtained by analyzing the wavelet coefficients of the original image. At each band of the wavelet decomposition, we compute an indicator of similarity between two objects. All the indicators are then fused using some rules of evidence theory to derive a unique criterion of similarity between two objects.

Citation Download Citation

Antoine Lefebvre, Thomas Corpetti, and Laurence Hubert Moy "Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300E (22 October 2010); https://doi.org/10.1117/12.864802

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available