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A new classification method for semi-arid regions based on sar and lidar data fusion

Iervolino, Pasquale, Coppola, Alessandro, Guida, Raffaella and Riccio, Daniele (2019) A new classification method for semi-arid regions based on sar and lidar data fusion In: International Geoscience & Remote Sensing Symposium (IGARSS) 2019, 2019-07-28-2019-08-02, Yokohama, Japan.

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Abstract

This paper aims at developing a new enhanced algorithm for mapping semi-arid areas based on fusion techniques of Synthetic Aperture Radar (SAR) and Light Detection And Ranging (LIDAR) datasets. Firstly, both datasets are preprocessed to remove geometric and radiometric errors; then features of interest are extracted from SAR and LiDAR products to build masks and identify meaningful classes. Finally, classification results are refined with morphological filters. The new algorithm has been tested on data acquired by TerraSAR-X and an airborne LiDAR sensor over the Natural Reserve of Maspalomas in Canary Islands. Results show an overall classification accuracy of 85% with an absolute increment of more than 14% compared to a classification in which only LiDAR data are used.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Surrey Space Centre
Authors :
NameEmailORCID
Iervolino, Pasqualep.iervolino@surrey.ac.uk
Coppola, Alessandro
Guida, RaffaellaR.Guida@surrey.ac.uk
Riccio, Daniele
Date : 27 May 2019
Copyright Disclaimer : Copyright ©2019 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Uncontrolled Keywords : SAR; LiDAR; Land classification; SAR texture; Gray Level Co-occurrence Matrix
Related URLs :
Additional Information : Paper identifier TH1.R11.4
Depositing User : Diane Maxfield
Date Deposited : 27 Aug 2019 12:21
Last Modified : 28 Oct 2019 11:18
URI: http://epubs.surrey.ac.uk/id/eprint/852479

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