SEGMENTATION OF ELECTRICAL SUBSTATIONS USING DEEP CONVOLUTIONAL NEURAL NETWORK

The location of electrical substations is one of the factors affecting the improvement of electrical energy distribution, as well as the management and control of this energy source. Less cost and manpower will be spent through automating the process of detection and segmentation of these features w...

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Vydané v:ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Ročník X-4/W1-2022; s. 495 - 500
Hlavní autori: Mesvari, M., Shah-Hosseini, R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Gottingen Copernicus GmbH 01.01.2023
Copernicus Publications
Predmet:
Artificial neural networks
Coders
Deep learning
Electrical transmission
Encoders-Decoders
Energy distribution
Energy sources
High resolution
Image processing
Image resolution
Image segmentation
Machine learning
Manpower
Neural networks
Satellite imagery
Spatial resolution
Substations
Teaching methods
ISSN:2194-9050, 2194-9042, 2194-9050
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Shrnutí:The location of electrical substations is one of the factors affecting the improvement of electrical energy distribution, as well as the management and control of this energy source. Less cost and manpower will be spent through automating the process of detection and segmentation of these features with the help of deep neural networks and the potential of existing high spatial resolution satellite images. In this study, a deep encoder-decoder neural network was used. This network is one of the most updated deep learning methods in image processing and segmentation. This network has been trained in three RGB bands with the help of high-resolution satellite images (∼1m) and eventually segmented the areas related to electrical substations with relatively high accuracy. As the results of this convolutional neural network, the IOU and Precision parameters were obtained, and their values were 88.2 and 93.7%, respectively, indicating the efficiency of the proposed deep learning method in the segmentation of existing satellite images.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2194-9050
2194-9042
2194-9050
DOI:10.5194/isprs-annals-X-4-W1-2022-495-2023