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Optimized unsupervised CORINE Land Cover mapping using linear spectral mixture analysis and object-based image analysis
- Source :
- Egyptian Journal of Remote Sensing and Space Sciences, Vol 24, Iss 3, Pp 1061-1069 (2021)
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- In this paper, the approach Linear Spectral Mixture Analysis and Object-Based Image Analysis (LSMA + OBIA) and Iterative Self-Organizing Data Analysis Technique and Object-Based Image Analysis (ISODATA + OBIA) are evaluated, for optimizing land cover mapping in high mountain areas from Landsat-8 multispectral images. Both approaches are applied to generate in a semiautomatic and unsupervised way a land cover map of the Santurbán-Berlín moorland, located in Colombia as a case study to carry out the evaluation. It has been found that LSMA + OBIA allows the generation of a land cover classification with a maximum global reliability of 88% compared to a reliability of 79% with ISODATA + OBIA.
- Subjects :
- Land cover
LSMA
OBIA
Moorland
Remote sensing
Landsat
Geodesy
QB275-343
Subjects
Details
- Language :
- English
- ISSN :
- 11109823
- Volume :
- 24
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Egyptian Journal of Remote Sensing and Space Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.22453df904844b3d85df4ec0c4c33b90
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.ejrs.2021.10.009