Abstract: Traditional road segmentation based on spectral features or morphological algorithms has some disadvantages such as low precision and difficulty in determining the threshold value, and the existing methods in deep learning do not consider the characteristics of roads, only using general methods to segment roads. A deep learning loss function named morphological loss function with road unique trait was proposed. Firstly, the connectivity algorithm was used to divide the prediction results into several separated connected regions, and the ratios of the region area to the circumscribed circle area was calculated respectively. Then, the average value of regions was taken as the morphological loss function of this batch of training data. Finally, the morphological loss function was summed with the cross entropy loss function according to a certain proportion to obtain the final loss function. Through the comparative experiment on open remote sensing dataset, MIoU, ACC and F1-Score were all improved by the addition of morphological loss function. According to the prediction image, the predicted road was more continuous when morphological loss function was added. The morphological loss function proposed is an effective method to improve the accuracy of road segmentation in remote sensing.

Key words: morphology, remote sensing image, road segmentation, semantic segmentation, deep learning