Por favor, use este identificador para citar o enlazar este ítem:
https://doi.org/10.3390/rs16122150
![](/digitum/image/email_logo.png)
![](/digitum/image/logo-facebook.png)
Registro completo de metadatos
Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Alonso Sarría, Francisco | - |
dc.contributor.author | Valdivieso Ros, Carmen | - |
dc.contributor.author | Gomariz Castillo, Francisco | - |
dc.contributor.other | Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Geografía | - |
dc.date.accessioned | 2024-07-03T10:09:04Z | - |
dc.date.available | 2024-07-03T10:09:04Z | - |
dc.date.issued | 2024-06-13 | - |
dc.identifier.citation | Remote Sensing, 2024, Vol. 16 (12): 2150 | es |
dc.identifier.issn | Electronic: 2072-4292 | - |
dc.identifier.uri | http://hdl.handle.net/10201/142824 | - |
dc.description | © 2024 by the authors. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published version of a Published Work that appeared in final form in Remote Sensing. To access the final edited and published work see https://doi.org/10.3390/rs16122150 | - |
dc.description.abstract | The availability of high spatial and temporal resolution imagery, such as that provided by the Sentinel satellites, allows the use of image time series to classify land cover. Recurrent neural networks (RNNs) are a clear candidate for such an approach; however, the presence of clouds poses a difficulty. In this paper, random forest (RF) and RNNs are used to reconstruct cloud-covered pixels using data from other next in time images instead of pixels in the same image. Additionally, two RNN architectures are tested to classify land cover from the series, treating reflectivities as time series and also treating spectral signatures as time series. The results are compared with an RF classification. The results for cloud removal show a high accuracy with a maximum RMSE of 0.057 for RNN and 0.038 for RF over all images and bands analysed. In terms of classification, the RNN model obtained higher accuracy (over 0.92 in the test data for the best hyperparameter combinations) than the RF model (0.905). However, the temporal–spectral model accuracies did not reach 0.9 in any case. | es |
dc.format | application/pdf | es |
dc.format.extent | 25 | es |
dc.language | eng | es |
dc.publisher | MDPI | es |
dc.relation | Grant TED2021-131131B-I00 funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. | es |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Sentinel-2 | es |
dc.subject | Cloud removal | es |
dc.subject | LSTM | es |
dc.subject.other | CDU::9 - Geografía e historia | es |
dc.title | Imagery time series cloud removal and classification using long short term memory neural networks | es |
dc.type | info:eu-repo/semantics/article | es |
dc.relation.publisherversion | https://www.mdpi.com/2072-4292/16/12/2150 | - |
dc.identifier.doi | https://doi.org/10.3390/rs16122150 | - |
Aparece en las colecciones: | Artículos: Geografía |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2024_RemoteSensing.pdf | Open Access version | 16,74 MB | Adobe PDF | ![]() Visualizar/Abrir |
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons