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A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification
Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) da...
Tallennettuna:
| Julkaisussa: | Sensors (Basel) |
|---|---|
| Päätekijät: | , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
MDPI
2019
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6891785/ https://ncbi.nlm.nih.gov/pubmed/31726726 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s19224927 |
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