Classification of summer crops using Active Learning techniques on Landsat images in the Northwest of the Province of Buenos Aires
The present work aims to obtain a classifier for summer crops in the northwest of Buenos Aires province from Landsat satellite images. Active Learning (AL) was used as the classification technique since it obtains satisfactory results using a small set of labeled samples to train the algorithm. The...
Guardado en:
| Autores principales: | , , |
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| Otros Autores: | |
| Formato: | Artículo publishedVersion |
| Lenguaje: | Inglés |
| Publicado: |
Springer, Cham.
2021
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| Materias: | |
| Acceso en línea: | https://repositorio.unnoba.edu.ar/xmlui/handle/23601/151 |
| Aporte de: |
| Sumario: | The present work aims to obtain a classifier for summer crops in the northwest of Buenos Aires province from Landsat satellite images. Active Learning (AL) was used as the classification technique since it obtains satisfactory results using a small set of labeled samples to train the algorithm. The construction of the training set is iteratively performed by means of a heuristic for the selection of the unlabeled samples to be classified by an expert. The following heuristics were used for comparison: Breaking Ties, Multiclass Level Uncertainty, Margin Sampling, and Random Sampling. The algorithm was also compared with the supervised technique Support Vector Machine (SVM). The experiments were tested on three Landsat 8 images from different dates using 6 bands per image and various vegetation indices. The results obtained using AL in combination with the different heuristics do not differ substantially from SVM. |
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