Land cover to habitat map conversion using remote sensing data: A supervised learning approach (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Land cover to habitat map conversion using remote sensing data: A supervised learning approach (Contributo in atti di convegno) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1109/IGARSS.2014.6947538 (literal)

Alternative label

• Petrou Z.I.; Stathaki T.; Manakos I.; Adamo M.; Tarantino C.; Blonda P. (2014)
  Land cover to habitat map conversion using remote sensing data: A supervised learning approach
  in IEEE International Conference on Geosc. and Remote Sensing Symposium IGARSS 2014, Quebec, Canada, 15th -18th July 2014
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Petrou Z.I.; Stathaki T.; Manakos I.; Adamo M.; Tarantino C.; Blonda P. (literal)

Pagina inizio

• 4683 (literal)

Pagina fine

• 4686 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.scopus.com/inward/record.url?eid=2-s2.0-84911385780&partnerID=q2rCbXpz (literal)

Note

• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom; Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Xarilaou Thermi, Thessaloniki, 57001, Greece; Institute for Studies on Intelligent System for Automation (ISSIA), National Research Council (CNR), Via Amendola 122, Bari, D-O 70126, Italy (literal)

Titolo

• Land cover to habitat map conversion using remote sensing data: A supervised learning approach (literal)

Abstract

• The derivation of habitat maps is enhanced if land cover maps are used as basis for the mapping procedure. In this study, a supervised learning framework is proposed to perform object-based classification to General Habitat Categories. A Land Cover Classification System map is used as basis, and an approach to generate numerical features from the object land cover class names and attributes is introduced. An additional number of spectral, morphological, and topological features are extracted from very high resolution satellite imagery and classification accuracies up to 80.4% for 14 classes are reached. Inclusion of LiDAR (Light Detection And Ranging) data or proposed texture analysis features, improve accuracies to 86% and around 83%, respectively, with the latter proving as promising surrogates of LiDAR data features. The method outperformed rule-based approaches, indicating its potential in accurate and labor- and time-efficient habitat classification. (literal)

Prodotto di

• Studio e Sviluppo di metodologie osservative da piattaforme in situ, aeree e satellitari per il monitoraggio ambientale (TA.P06.024.001) (Modulo)
• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR

• MARIA ADAMO (Unità di personale interno)
• PALMA NICOLETTA BLONDA (Unità di personale interno)
• CRISTINA TARANTINO (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Land cover to habitat map conversion using remote sensing data: A supervised learning approach" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)
• Studio e Sviluppo di metodologie osservative da piattaforme in situ, aeree e satellitari per il monitoraggio ambientale (TA.P06.024.001) (Modulo)

Autore CNR di

• PALMA NICOLETTA BLONDA (Unità di personale interno)
• CRISTINA TARANTINO (Unità di personale interno)
• MARIA ADAMO (Unità di personale interno)

Insieme di parole chiave di

• Keywords of "Land cover to habitat map conversion using remote sensing data: A supervised learning approach" (Insieme di parole chiave)