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UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle

Kestur, Ramesh and Farooq, Shariq and Abdal, Rameen and Mehraj, Emad and Narasipura, Omkar and Mudigere, Meenavathi (2018) UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle. In: JOURNAL OF APPLIED REMOTE SENSING, 12 .

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Official URL: http://dx.doi.org/10.1117/1.JRS.12.016020

Abstract

Road extraction in imagery acquired by low altitude remote sensing (LARS) carried out using an unmanned aerial vehicle (UAV) is presented. LARS is carried out using a fixed wing UAV with a high spatial resolution vision spectrum (RGB) camera as the payload. Deep learning techniques, particularly fully convolutional network (FCN), are adopted to extract roads by dense semantic segmentation. The proposed model, UFCN (U-shaped FCN) is an FCN architecture, which is comprised of a stack of convolutions followed by corresponding stack of mirrored deconvolutions with the usage of skip connections in between for preserving the local information. The limited dataset (76 images and their ground truths) is subjected to real-time data augmentation during training phase to increase the size effectively. Classification performance is evaluated using precision, recall, accuracy, F1 score, and brier score parameters. The performance is compared with support vector machine (SVM) classifier, a one-dimensional convolutional neural network (1D-CNN) model, and a standard two-dimensional CNN (2D-CNN). The UFCN model outperforms the SVM, 1D-CNN, and 2D-CNN models across all the performance parameters. Further, the prediction time of the proposed UFCN model is comparable with SVM, 1D-CNN, and 2D-CNN models. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

Item Type: Journal Article
Additional Information: Copy right for the article belong to SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Depositing User: Id for Latest eprints
Date Deposited: 14 Mar 2018 17:39
Last Modified: 14 Mar 2018 17:39
URI: http://eprints.iisc.ac.in/id/eprint/59171

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