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Article

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

Universidad Politécnica de Madrid, 28031 Madrid, Spain
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Remote Sens. 2020, 12(5), 765; https://doi.org/10.3390/rs12050765
Received: 17 January 2020 / Revised: 23 February 2020 / Accepted: 25 February 2020 / Published: 27 February 2020
(This article belongs to the Section Remote Sensing Image Processing)
Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often 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) to classify 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 with those obtained by other popular CNNs trained from scratch. View Full-Text
Keywords: road classification; convolutional neural networks; remote sensing; image analysis; secondary transport routes; deep learning road classification; convolutional neural networks; remote sensing; image analysis; secondary transport routes; deep learning
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MDPI and ACS Style

Cira, C.-I.; Alcarria, R.; Manso-Callejo, M.-Á.; Serradilla, F. A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages. Remote Sens. 2020, 12, 765. https://doi.org/10.3390/rs12050765

AMA Style

Cira C-I, Alcarria R, Manso-Callejo M-Á, Serradilla F. A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages. Remote Sensing. 2020; 12(5):765. https://doi.org/10.3390/rs12050765

Chicago/Turabian Style

Cira, Calimanut-Ionut, Ramon Alcarria, Miguel-Ángel Manso-Callejo, and Francisco Serradilla. 2020. "A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages" Remote Sensing 12, no. 5: 765. https://doi.org/10.3390/rs12050765

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