乐胖代购免代理版

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(2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (arXiv:2010.11929). arXiv. http:\/\/arxiv.org\/abs\/2010.11929."},{"key":"10.1016\/j.eswa.2024.125132_b0050","doi-asserted-by":"crossref","first-page":"10041","DOI":"10.1109\/JSTARS.2022.3221098","article-title":"MSLAENet: Multiscale learning and attention enhancement network for fusion classification of hyperspectral and LiDAR data","volume":"15","author":"Fan","year":"2022","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"10.1016\/j.eswa.2024.125132_b0055","first-page":"1","article-title":"S2ENet: Spatial-spectral cross-modal enhancement network for classification of hyperspectral and LiDAR data","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.eswa.2024.125132_b0060","first-page":"1","article-title":"Multi-complementary generative adversarial networks with contrastive learning for hyperspectral image classification","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2024.125132_b0065","unstructured":"Gader, P., Zare, A., & Close, R. 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