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Authors: Lakshya Garg 1 ; Parul Shukla 2 ; Sandeep Singh 2 ; Vaishangi Bajpai 2 and Utkarsh Yadav 2

Affiliations: 1 Electrical and Electronics Engineering, Delhi Technological University (DTU), Delhi and India ; 2 Strategic Operations and Research, RMSI Pvt Ltd Noida, Delhi and India

ISBN: 978-989-758-354-4

Keyword(s): Satellite Imagery, Land-use-classification, Convolutional Networks, Remote Sensing, Deep Learning.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: Land-use-land-cover classification(LULC) is used to automate the process of providing labels, describing the physical land type to represent how a land area is being used. Many sectors such as telecom, utility, hydrology etc need land use and land cover information from remote sensing images. This information provides an insight into the type of geographical distribution of a region with providing low level features such as amount of vegetation, building area, and geometry etc as well as higher level concepts such as land use classes. This information is particularly useful for resource-starved rapidly developing cities for urban planning and resource management. LULC also provides historical changes in land-use patterns over a period of time. In this paper, we analyze patterns of land use in urban and rural neighborhoods using high resolution satellite imagery, utilizing a state of the art deep convolutional neural network. The proposed LULC network, termed as mUnet is based on an en coder-decoder convolutional architecture for pixel-level semantic segmentation. We test our approach on 3 band, FCC satellite imagery covering 225 km2 area of Karachi. Experimental results show the superiority of our proposed network architecture vis-à-vis other state of the art networks. (More)

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Paper citation in several formats:
Garg, L.; Shukla, P.; Singh, S.; Bajpai, V. and Yadav, U. (2019). Land Use Land Cover Classification from Satellite Imagery using mUnet: A Modified Unet Architecture.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 359-365. DOI: 10.5220/0007370603590365

@conference{visapp19,
author={Lakshya Garg. and Parul Shukla. and Sandeep Kumar Singh. and Vaishangi Bajpai. and Utkarsh Yadav.},
title={Land Use Land Cover Classification from Satellite Imagery using mUnet: A Modified Unet Architecture},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,},
year={2019},
pages={359-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007370603590365},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,
TI - Land Use Land Cover Classification from Satellite Imagery using mUnet: A Modified Unet Architecture
SN - 978-989-758-354-4
AU - Garg, L.
AU - Shukla, P.
AU - Singh, S.
AU - Bajpai, V.
AU - Yadav, U.
PY - 2019
SP - 359
EP - 365
DO - 10.5220/0007370603590365

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