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Unsupervised Detection of Surface Mine Sites using Sentinel Multi-spectral Imagery and Dual-polarimetric SAR Data

Published: 03 May 2020 Publication History

Abstract

Detecting surface mine activities has been one of the major challenges of remote sensing community to monitor ecological balance in the vicinity of mine area. Towards addressing this issue, we propose an unsupervised mine-site detection technique using multi-modal remotely sensed data from Sentinel missions. We use Sentinel-1's dual polarized SAR data, and Sentinel-2's multi-spectral data for this purpose. An initial set of seed pixels is sampled using spectral slopes based rules on multi-spectral images, which are tailored to capture mining area pixels that exhibit common characteristics. Then, we generate features using multi-spectral image indices and dual polarized SAR data Stokes' parameters. These features over locations sampled by slope based rules are used to train a one-class support vector machine to detect the land anomalies (mine activities). As observed from the experiments, multi-spectral features are efficient in separating mine region and built-up region from the rest of the land surface, and SAR image features discriminate built-up region from the rest of the image. We fuse both of these imagery features to encash their advantage in segregating mine sites in the considered study area. We demonstrate the efficiency of the proposed technique in detecting active coal mines over two study sites, Asansol region (in West bengal, India) and Jharia region (in Jharkhand, India), both of which exhibit common geographical characteristics. The average detection accuracy in our results is found to be around 94.8%.

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Cited By

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  • (2023)A Characterization of Land-use Changes in the Proximity of Mining Sites in IndiaACM Journal on Computing and Sustainable Societies10.1145/36247742:1(1-23)Online publication date: 27-Sep-2023
  • (2023)Detection of Coal Quarry and Coal Dump Regions Using the Presence of Mine Water Bodies from Landsat 8 OLI/TIRS ImagesComputer Vision and Image Processing10.1007/978-3-031-31417-9_15(190-204)Online publication date: 7-May-2023
  • (2021)Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fieldsMultimedia Tools and Applications10.1007/s11042-020-10479-380:28-29(35605-35627)Online publication date: 1-Nov-2021

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cover image ACM Other conferences
ICVGIP '18: Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing
December 2018
659 pages
ISBN:9781450366151
DOI:10.1145/3293353
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 03 May 2020

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Author Tags

  1. Multi-spectral imagery
  2. multi-modal analysis
  3. spectral slopes
  4. surface mining area detection
  5. synthetic aperture radar

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ICVGIP 2018

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

View all
  • (2023)A Characterization of Land-use Changes in the Proximity of Mining Sites in IndiaACM Journal on Computing and Sustainable Societies10.1145/36247742:1(1-23)Online publication date: 27-Sep-2023
  • (2023)Detection of Coal Quarry and Coal Dump Regions Using the Presence of Mine Water Bodies from Landsat 8 OLI/TIRS ImagesComputer Vision and Image Processing10.1007/978-3-031-31417-9_15(190-204)Online publication date: 7-May-2023
  • (2021)Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fieldsMultimedia Tools and Applications10.1007/s11042-020-10479-380:28-29(35605-35627)Online publication date: 1-Nov-2021

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