Classification of multitemporal SAR images using convolutional neural networks and Markov random fields

C. Danilla, C. Persello, Valentyn Tolpekin, J. R. Bergado

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)
Original languageUndefined
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Subtitle of host publication23-28 Jly 2017, Fort Worth Texas, USA
PublisherIEEE
Pages2231-2234
Number of pages4
ISBN (Electronic)978-1-5090-4951-6
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • Markov processes
  • geophysical image processing
  • image classification
  • learning (artificial intelligence)
  • neural nets
  • radar imaging
  • remote sensing by radar
  • synthetic aperture radar
  • terrain mapping
  • Flevoland
  • Markov Random Fields
  • Markov random fields
  • Sentinel-1 images
  • The Netherlands
  • agricultural field mapping
  • classification accuracy
  • classification system
  • complex task
  • convolutional neural networks
  • extract spatial-contextual features
  • land-cover map
  • multitemporal SAR images
  • multitemporal series
  • post-classification label
  • scattering mechanism
  • spatial filters
  • spatial-contextual features
  • speckle noise
  • strong noise
  • synthetic aperture radar images
  • texture mechanism
  • Classification algorithms
  • Feature extraction
  • Filtering
  • Radio frequency
  • Speckle
  • Support vector machines
  • Synthetic aperture radar
  • Convolutional neural networks
  • Sentinel-1
  • speckle filtering

Cite this

Danilla, C., Persello, C., Tolpekin, V., & Bergado, J. R. (2017). Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 Jly 2017, Fort Worth Texas, USA (pp. 2231-2234). IEEE. https://doi.org/10.1109/IGARSS.2017.8127432
Danilla, C. ; Persello, C. ; Tolpekin, Valentyn ; Bergado, J. R. / Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, 2017. pp. 2231-2234
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keywords = "Markov processes, geophysical image processing, image classification, learning (artificial intelligence), neural nets, radar imaging, remote sensing by radar, synthetic aperture radar, terrain mapping, Flevoland, Markov Random Fields, Markov random fields, Sentinel-1 images, The Netherlands, agricultural field mapping, classification accuracy, classification system, complex task, convolutional neural networks, extract spatial-contextual features, land-cover map, multitemporal SAR images, multitemporal series, post-classification label, scattering mechanism, spatial filters, spatial-contextual features, speckle noise, strong noise, synthetic aperture radar images, texture mechanism, Classification algorithms, Feature extraction, Filtering, Radio frequency, Speckle, Support vector machines, Synthetic aperture radar, Convolutional neural networks, Sentinel-1, speckle filtering",
author = "C. Danilla and C. Persello and Valentyn Tolpekin and Bergado, {J. R.}",
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Danilla, C, Persello, C, Tolpekin, V & Bergado, JR 2017, Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, pp. 2231-2234. https://doi.org/10.1109/IGARSS.2017.8127432

Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. / Danilla, C.; Persello, C.; Tolpekin, Valentyn; Bergado, J. R.

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, 2017. p. 2231-2234.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - Classification of multitemporal SAR images using convolutional neural networks and Markov random fields

AU - Danilla, C.

AU - Persello, C.

AU - Tolpekin, Valentyn

AU - Bergado, J. R.

PY - 2017/7/1

Y1 - 2017/7/1

KW - Markov processes

KW - geophysical image processing

KW - image classification

KW - learning (artificial intelligence)

KW - neural nets

KW - radar imaging

KW - remote sensing by radar

KW - synthetic aperture radar

KW - terrain mapping

KW - Flevoland

KW - Markov Random Fields

KW - Markov random fields

KW - Sentinel-1 images

KW - The Netherlands

KW - agricultural field mapping

KW - classification accuracy

KW - classification system

KW - complex task

KW - convolutional neural networks

KW - extract spatial-contextual features

KW - land-cover map

KW - multitemporal SAR images

KW - multitemporal series

KW - post-classification label

KW - scattering mechanism

KW - spatial filters

KW - spatial-contextual features

KW - speckle noise

KW - strong noise

KW - synthetic aperture radar images

KW - texture mechanism

KW - Classification algorithms

KW - Feature extraction

KW - Filtering

KW - Radio frequency

KW - Speckle

KW - Support vector machines

KW - Synthetic aperture radar

KW - Convolutional neural networks

KW - Sentinel-1

KW - speckle filtering

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BT - 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Danilla C, Persello C, Tolpekin V, Bergado JR. Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): 23-28 Jly 2017, Fort Worth Texas, USA. IEEE. 2017. p. 2231-2234 https://doi.org/10.1109/IGARSS.2017.8127432