The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures. Today, most research focuses on optimizing the deep learning network topology, however, the accuracy of segmentation is affected by the loss function used in training; currently, little research has been published on ad-hoc loss functions for road segmentation. To solve this problem, we proposed loss functions based on topological pixel analysis, in which more weight is given to problematic pixels representing non-real road breaks. We report the results of different tests, obtaining state-of-the-art performance among convolution neural network-based approaches. For instance, on the Massachusetts Roads dataset, our method achieved a Dice score of 75.34% and an IoU of 60.44%, compared to the best baseline scores of 74.64% and 59.51% achieved by GapLoss. Similarly, on the DeepGlobe Roads dataset, our method obtained a Dice score of 79.78% and an IoU of 66.36%, outperforming the best baseline scores of 78.62% and 64.47% by GapLoss. Both the code and information for replicating our experiments are available at https://github.com/LorisNanni/An-Enhanced-Loss-Function-for-Semantic-Road-Segmentation-in-Remote-Sensing-Images, so as to enable future reliable comparisons.
An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images
Loreggia A.
2024-01-01
Abstract
The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures. Today, most research focuses on optimizing the deep learning network topology, however, the accuracy of segmentation is affected by the loss function used in training; currently, little research has been published on ad-hoc loss functions for road segmentation. To solve this problem, we proposed loss functions based on topological pixel analysis, in which more weight is given to problematic pixels representing non-real road breaks. We report the results of different tests, obtaining state-of-the-art performance among convolution neural network-based approaches. For instance, on the Massachusetts Roads dataset, our method achieved a Dice score of 75.34% and an IoU of 60.44%, compared to the best baseline scores of 74.64% and 59.51% achieved by GapLoss. Similarly, on the DeepGlobe Roads dataset, our method obtained a Dice score of 79.78% and an IoU of 66.36%, outperforming the best baseline scores of 78.62% and 64.47% by GapLoss. Both the code and information for replicating our experiments are available at https://github.com/LorisNanni/An-Enhanced-Loss-Function-for-Semantic-Road-Segmentation-in-Remote-Sensing-Images, so as to enable future reliable comparisons.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
