A comparative study of loss functions for road segmentation in remotely sensed road datasets
Hongzhang Xu, Hongjie He, Ying Zhang, Lingfei Ma, Jonathan Li
Abstract
Road extraction from remote sensing imagery is a fundamental task in the field of image semantic segmentation. For this goal, numerous supervised deep learning techniques have been created, along with the employment of various loss functions that play a crucial role in determining the performances of supervised learning models. However, there is a lack of comprehensive analysis of the performance differences between the loss functions for road segmentation in remote sensing imagery. Therefore, this study conducts a comparative study of 12 well-known loss functions used widely in the field of image segmentation by training and evaluating the representative D-LinkNet network for road segmentation tasks with two publicly available remote sensing road datasets consisting of very high-resolution aerial and satellite images. The results show that different loss functions could lead to very different outcomes using the D-LinkNet, with varying focuses such as on overall model performances, precision, or recall. By dividing the loss functions into the distribution-based, region-based, and compound ones, we found that the region-based loss function type led to generally better model performances than the distribution-based one in terms of F1, IoU, and the road segmentation maps, with the compound loss function type being comparable to the region-based one. This paper eventually tries to offer suggestions for choosing the loss function that best suits the purposes of road segmentation-related studies.