联营
计算机科学
人工智能
编码器
计算复杂性理论
模式识别(心理学)
算法
操作系统
作者
R. Avudaiammal,Vandita Srivastava,Sam Varghese George,Swarnalatha Alagala,Martin Leo Manickam
标识
DOI:10.1080/14498596.2024.2302166
摘要
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.
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