Analysis of clustering methods for crop type mapping using satellite imagery
With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/7017 |
| Acceso en línea: | https://www.sciencedirect.com/science/article/pii/S0925231222003691 https://hdl.handle.net/10953/7017 |
| Access Level: | acceso abierto |
| Palabra clave: | Unsupervised learning Clustering Crop mappings Time series Classification Satellite imagery 004 004.3 004.6 004.8 |
| Sumario: | With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the ongoing increment on the spatial–temporal resolution together with the free availability of some of these services. Typically time series are used as a pre-processing technique and combined with supervised learning techniques in order to build models for crop type identification in remote images. However, these models suffer from the lack of labelled data sets needed to train them. Unsupervised classification can overcome this limitation but has been less frequently used in this research field. This paper proposes to test and analyse the performance of several unsupervised clustering algorithms towards crop type identification on remote images. In this manner combinations of clustering algorithms and distance measures, a key element in the behaviour of these algorithms, are studied using an experimental design with more than twenty datasets built from the combinations of five crops and more than 45000 parcels. Results highlight better clustering methods and distance measures to create accurate and novel crop mapping models for remote sensing images. |
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