Road extraction in RGB images acquired by low altitude remote sensing from an unmanned aerial vehicle: A neural network based approach

Ramesh, KN and Yogitha, AN and Ravi, VMhttps://a-a-r-s.org/proceeding/ACRS2017/ID_753_1652/1425.pdf and Omkar, SN and Meenavathi, MB (2017) Road extraction in RGB images acquired by low altitude remote sensing from an unmanned aerial vehicle: A neural network based approach. In: 38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017, 23 - 27 October 2017, New Delhi.

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Abstract

The high growth rate of urban population has led to an increase in the demand for better urban planning and monitoring which mainly includes road network development. Manual monitoring of road development is time consuming and inefficient. In this paper, we propose a method for automatic extraction of roads in vision spectrum (RGB) images acquired by remote sensing from a UAV also known as Low Altitude Remote Sensing (LARS) or Near Earth Remoste Sensing . Extreme Learning Machine (ELM), a neural networks based classier is used for spectral classication. Spectral classication is further improved by applying spatial techniques. The spatial techniques include a combination of Shape Index(SI), Density Index(DI) and mathematical morphological close operations. Seven images of diverse road stretches are analyzed to verify the robustness of the proposed method. The classification results are analysed using confusion matrix. The performance parameters derived from confusion matrix are analyzed for a range of hidden neurons of the ELM model and an optimum number of hidden neurons are chosen. Successful road extraction demonstrates the potential of using UAV imagery for monitoring road development.

Item Type:	Conference Paper
Publication:	38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017
Publisher:	Asian Association on Remote Sensing
Additional Information:	The copyright for this article belongs to Asian Association on Remote Sensing.
Keywords:	Antennas; Color image processing; Extraction; Feature extraction; Image acquisition; Image resolution; Knowledge acquisition; Learning systems; Mathematical morphology; Neural networks; Population statistics; Roads and streets; Space applications; Space optics; Unmanned aerial vehicles (UAV); Urban growth, Automatic extraction; Classification results; Extreme learning machine; Morphological close; Network-based approach; Performance parameters; Planning and monitoring; Spatial methods, Remote sensing
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	27 Jul 2022 05:55
Last Modified:	27 Jul 2022 05:55
URI:	https://eprints.iisc.ac.in/id/eprint/74678

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