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High level semantic land cover classification of multitemporal SAR images using synergic pixel-based and object-based methods

Amitrano, Donato, Guida, Raffaella and Iervolino, Pasquale (2019) High level semantic land cover classification of multitemporal SAR images using synergic pixel-based and object-based methods In: International Geoscience & Remote Sensing Symposium (IGARSS) 2019, 2019-07-28-2019-08-02, Yokohama, Japan.

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Abstract

Land cover mapping is one of the classic applications of synthetic aperture radar remote sensing. However, despite of the algorithmic progress in classification techniques, the semantic content of available maps does remain unchanged, with only a few macro-classes (like water, forest, urban, and bare soil) being discriminated in the majority of the works from past years. In this paper, a methodology to extract a higher level semantics from synthetic aperture radar images is presented. It is based on coupling pixel-based clustering with object-based image analysis and contextual information. Preliminary results have been produced from multitemporal SAR datasets over a forest area in Colombia. They demonstrate that the synergic exploitation of pixel and object information can provide higher quality land cover results and more information to map users.

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Surrey Space Centre
Authors :
NameEmailORCID
Amitrano, Donatod.amitrano@surrey.ac.uk
Guida, RaffaellaR.Guida@surrey.ac.uk
Iervolino, Pasqualep.iervolino@surrey.ac.uk
Date : 27 May 2019
Copyright Disclaimer : Copyright ©2019 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Uncontrolled Keywords : Synthetic aperture radar; Land cover classification; High-level semantics; Multitemporal; Object-based image analysis
Related URLs :
Additional Information : Paper identifier WEP1.PF.1
Depositing User : Diane Maxfield
Date Deposited : 27 Aug 2019 14:33
Last Modified : 28 Oct 2019 11:09
URI: http://epubs.surrey.ac.uk/id/eprint/852481

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