Convolutional encoder-decoder network for road extraction from remote sensing images

Yasmine Makhlouf, Abdelhamid Daamouche, Farid Melgani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a convolutional neural network, which is based on down sampling followed by up sampling architecture for the purpose of road extraction from aerial images. Our model consists of convolutional layers only. The proposed encoder-decoder structure allows our network to retain boundary information, which is a critical feature for road identification. This feature is usually lost when dealing with other CNN models. Our design is also less complex in terms of depth, number of parameters, and memory size. It, therefore, uses fewer computer resources in both training and during execution. Experimental results on Massachusetts roads dataset demonstrate that the proposed architecture, although less complex, competes with the state-of-the-art proposed approaches in terms of precision, recall, and accuracy.
Original languageEnglish
Title of host publication2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS): Proceedings
Pages11-15
ISBN (Electronic)9798350358582
DOIs
Publication statusPublished - 27 May 2024

Publication series

NameIEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS): Proceedings
PublisherIEEE Xplore

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