Convolutional Neural Network with Fourier Transform for Road Classification from Satellite Images

Hormese, Jose; Chandran, Saravanan

doi:10.1007/978-3-030-41862-5_127

Cited by 1 publication

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among several existing models, we can, also, cite the deep CNN based-on model that uses a cascade architecture with two levels to detect roads and then extract centerlines by using the domain area knowledge [7]. On the other hand, the model that uses classification method to detect road from satellite images with the help of CNN by using AlexNet with 128×128 resolutions in the Fourier domain, achieves also good results [8]. Further, another interesting model is the RoadTracer [9].…”

Section: Introductionmentioning

confidence: 99%

A new approach for road extraction using data augmentation and semantic segmentation

Babaali

Zigh

Djebbouri

et al. 2022

IJEECS

View full text Add to dashboard Cite

Accurate road extraction from remote sensing images is a challenging task. Several methods of extraction have been developed but the precision of extraction is still limited for the unpaved and small-width roads. This paper proposes an accurate road extraction approach called DAA-SSEG since it uses data augmentation architecture (DAA) and semantic segmentation model (SSEG). The proposed approach DAA-SSEG is based on a modified full convolutional neural network that overcomes the vanishing gradient and the training saturation issues. It recognizes roads at the pixel level. Furthermore, The DAA-SSEG approach uses a new plan of data augmentation based on geometric transformation and images refinement techniques. It allows getting a richer dataset thus better training and an accurate extraction. The experiment denotes that the proposed approach DAA-SSEG, that combine data augmentation architecture and semantic segmentation method, outperforms some state-of-the-art methods in terms of F-measures. The results demonstrate that it ensures accurate extraction of unpaved and small-width roads, in urban and rural areas. Moreover, the proposed approach distinguishes between roads and trails and can extract some roads not labeled beforehand.

show abstract

Section: Introductionmentioning

confidence: 99%