Multisensor fusion for the accurate classification of vegetation in complex ecosystems

Marcello Ruiz, Javier and Rodríguez Esparragón, Dionisio and Ibarrola Ulzurrun, Edurne and Gonzalo Martín, Consuelo (2019). Multisensor fusion for the accurate classification of vegetation in complex ecosystems. In: "IEEE International Work Conference on Bioinspired Intelligence (IWOBI 2019)", 03-05 Jul 2019, Budapest, Hungary. pp. 81-86. https://doi.org/10.1109/IWOBI47054.2019.9114397.

Description

Title: Multisensor fusion for the accurate classification of vegetation in complex ecosystems
Author/s:
  • Marcello Ruiz, Javier
  • Rodríguez Esparragón, Dionisio
  • Ibarrola Ulzurrun, Edurne
  • Gonzalo Martín, Consuelo
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE International Work Conference on Bioinspired Intelligence (IWOBI 2019)
Event Dates: 03-05 Jul 2019
Event Location: Budapest, Hungary
Title of Book: 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)
Date: 2019
Subjects:
Freetext Keywords: Remote sensing, Hyperspectral, Sharpening, Classification, Ecosystems
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The use of geospatial tools to monitor natural ecosystems is a fundamental task to preserve the environment. In this context, remote sensing data can provide a valuable source of information to complement field observations, offering frequent and accurate imagery to support the mapping and monitoring of natural areas. The growing availability of hyperspectral (HS) data can provide a valuable solution but the spectral richness provided by hyperspectral sensors is usually at the expense of spatial resolution. To alleviate this inconvenience, instead of satellite platforms, airborne sensors can be considered. In this work, the accurate mapping of a complex shrubland ecosystem has been accomplished using multisensor imagery. Specifically, airborne CASI data (68 bands and 75 cm of pixel size) has been fused with an orthophoto (25 cm) to increase the spatial detail. A comprehensive analysis of 11 sharpening algorithms has been performed and, to improve the Support Vector Machine (SVM) classification accuracy, different input features have been considered. Excellent results have been achieved and the importance to improve the spatial resolution has been demonstrated.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainCTM2016-77733-RARTEMISAT-2UnspecifiedProcesado avanzado de datos de teledetección para la monitorización y gestión sostenible de recursos marinos y terrestres en ecosistemas vulnerables

More information

Item ID: 66948
DC Identifier: https://oa.upm.es/66948/
OAI Identifier: oai:oa.upm.es:66948
DOI: 10.1109/IWOBI47054.2019.9114397
Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9114397
Deposited by: Memoria Investigacion
Deposited on: 07 May 2021 08:13
Last Modified: 07 May 2021 08:13
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