Comparison of very high spatial resolution satellite image segmentations

Alexandre P. Carleer; Olivier Debeir; Eleonore Wolff

doi:10.1117/12.511027

5 February 2004 Comparison of very high spatial resolution satellite image segmentations

Alexandre P. Carleer, Olivier Debeir, Eleonore Wolff

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.511027
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

Since 1999, very high spatial resolution data represent the surface of the earth with more details. However, information extraction by computer-assisted classification techniques proves to be very complex owing to the internal variability increase in land-cover units and to the weakness of spectral resolution. The increase in variability decreases the statistical separability of land-cover classes in the spectral space. Per pixel multispectral classification techniques are then insufficient for an extraction of complex categories and spectrally heterogeneous land-cover, like urban areas. Per region classification was proposed as an alternative approach. The first step of this approach is the segmentation. A large variety of segmentation algorithms were developed these last 20 years and a comparison of their implementation on very high spatial resolution images is necessary. For this study, four algorithms from the two main groups of segmentation algorithms (boundary-based and region-based algorithms) were selected. In order to compare the algorithms, an evaluation of each algorithm was carried out with empirical discrepancy evaluation methods. This evaluation is carried out with a visual segmentation of IKONOS panchromatic images.

Citation Download Citation

Alexandre P. Carleer, Olivier Debeir, and Eleonore Wolff "Comparison of very high spatial resolution satellite image segmentations", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.511027

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