Szirányi, Tamás and Shadaydeh, Maha (2014) Segmentation of remote sensing images using similarity measure based fusion-MRF model. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 11 (9). pp. 1544-1548. ISSN 1545-598X 10.1109/LGRS.2014.2300873 (In Press)

Preview

Text IEEE-GRSL_2014_SzT-SM_AuthorsCopy.pdf - Accepted Version Download (886kB) | Preview

Abstract

Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies nsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically alidated on remotely sensed image series with ground-truth data.

Item Type:	ISI Article
Subjects:	Q Science > QA Mathematics and Computer Science Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Distributed Events Analysis Research Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	15 May 2014 11:12
Last Modified:	19 Nov 2015 00:15
URI:	https://eprints.sztaki.hu/id/eprint/7804

Update Item