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Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community—can a limited amount of highly-discriminative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discrimin...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360532/ https://www.ncbi.nlm.nih.gov/pubmed/30774220 http://dx.doi.org/10.1016/j.isprsjprs.2018.10.006 |