Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69802
Title: Evaluation of hyperspectral classification maps in heterogeneous ecosystem
Authors: Ibarrola-Ulzurrun, Edurne 
Marcello, Javier 
Gonzalo Martin,Consuelo 
Chanussot, Jocelyn
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Binary Partition Tree
Casi Sensor
Ecosystem Management
Hyperspectral Imagery
Support Vector Machine
Issue Date: 2018
Project: Procesado Avanzado de Datos de Teledetección Para la Monitorización y Gestión Sostenible de Recursos Marinos y Terrestres en Ecosistemas Vulnerables. 
Journal: IEEE International Geoscience and Remote Sensing Symposium proceedings 
Conference: 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 
Abstract: Ecosystem management and monitoring are essential to preserve natural resources. Hyperspectral imagery (HSI) is a useful tool to obtain accurate classification maps, providing significant level of detail. Thus, traditional and novel methodologies based on pixel and object classification approaches are compared and evaluated in a homogeneous and mixed vulnerable ecosystem. Considering the challenging ecosystem, all classifications successfully resulted in high OA (higher than 82%), showing that HSI is very useful providing accurate vegetation maps to evaluate and monitor the ecosystems in a faster and economic way.
URI: http://hdl.handle.net/10553/69802
ISBN: 9781538671504
ISSN: 2153-6996
DOI: 10.1109/IGARSS.2018.8518308
Source: International Geoscience and Remote Sensing Symposium (IGARSS) [2153-6996],v. 2018-July, p. 5764-5767
Appears in Collections:Actas de congresos
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