A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

Cira, Calimanut-ionut and Alcarria Garrido, Ramón Pablo and Manso Callejo, Miguel Ángel and Serradilla García, Francisco (2020). A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages. "Remote Sensing" (n. 12); pp. 765-787. ISSN 2072-4292. https://doi.org/10.3390/rs12050765.

Description

Title: A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages
Author/s:
  • Cira, Calimanut-ionut
  • Alcarria Garrido, Ramón Pablo
  • Manso Callejo, Miguel Ángel
  • Serradilla García, Francisco
Item Type: Article
Título de Revista/Publicación: Remote Sensing
Date: 2020
ISSN: 2072-4292
Subjects:
Freetext Keywords: road classification; convolutional neural networks; remote sensing; image analysis; secondary transport routes; deep learning
Faculty: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Department: Ingeniería Cartográfica y Topografía
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Remote sensing imagery combined with deep learning strategies is often regarded as anideal solution for interpreting scenes and monitoring infrastructures with remarkable performancelevels. In addition, the road network plays an important part in transportation, and currently one ofthe main related challenges is detecting and monitoring the occurring changes in order to updatethe existent cartography. This task is challenging due to the nature of the object (continuous andoften with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions).In this paper, we propose a novel framework based on convolutional neural networks (CNNs) toclassify secondary roads in high-resolution aerial orthoimages divided in tiles of 256×256 pixels.We will evaluate the framework’s performance on unseen test data and compare the results withthose obtained by other popular CNNs trained from scratch.

More information

Item ID: 62513
DC Identifier: http://oa.upm.es/62513/
OAI Identifier: oai:oa.upm.es:62513
DOI: 10.3390/rs12050765
Official URL: https://www.mdpi.com/2072-4292/12/5/765
Deposited by: Memoria Investigacion
Deposited on: 27 Apr 2020 09:36
Last Modified: 29 Apr 2020 09:23
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