| Autor: |
Ramalingam, Avudaiammal, Srivastava, Vandita, George, Sam V, Alagala, Swarnalatha, Manickam, Martin Leo |
| Předmět: |
|
| Zdroj: |
Journal of Spatial Science; Sep2024, Vol. 69 Issue 3, p773-800, 28p |
| Abstrakt: |
Retrieved rooftops from satellite images have enormous applications. The diversity and complexity of the building structures is challenging. This work proposes to extract building rooftops using two low-complexity DL models: UNet-AstPPD and UNetVasyPPD. The UNet-AstPPD model enhances feature selection by incorporating Atrous Spatial Pyramidal Pooling into the UNet's decoder. The UNetVasyPPD integrates a VGG-based backbone in the encoder and Asymmetrical Pyramidal-Pooling into the decoder section of the UNet architecture, exhibiting lesser computational complexity. The outcomes demonstrate that Accuracy and Dice Loss of UNet-AstPPD are better. The proposed models training times are just 25.44 minutes and 29.23 minutes respectively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
