Abstract:
Nowadays, precise and up-to-date maps of road are of great signi¯cance in an extensive series of
applications. However, it automatically extracts the road surfaces from high-resolution remote
sensed images which will remain as a demanding issue owing to the occlusion of buildings, trees,
and intricate backgrounds. In order to address these issues, a robust Gradient Descent Sea Lion
Optimization-based U-Net (GDSLO-based U-Net) is developed in this research work for road
outward extraction from High Resolution (HR) sensing images. The developed GDSLO algorithm
is newly devised by the incorporation of Stochastic Gradient Descent (SGD) and Sea Lion
Optimization Algorithm (SLnO) algorithm. Input image is pre-processed and U-Net is
employed in road segmentation phase for extracting the road surfaces. Meanwhile, training data
of U-Net has to be done by using the GDSLO optimization algorithm. Once road segmentation
is done, road edge detection and road centerline detection is performed using Fully Convolutional
Network (FCN). However, the developed GDSLO-based U-Net method achieved superior
performance by containing the estimation criteria, including precision, recall, and F1-measure
through highest rate of 0.887, 0.930, and 0.809, respectively.
