Detection of informal settlements from VHR satellite images using convolutional neural networks

Nicholus Mboga, C. Persello, J. R. Bergado, A. Stein

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

3 Citations (Scopus)
Original languageUndefined
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Subtitle of host publication23-28 July 2017, Fort Worth Texas, USA
PublisherIEEE
Pages5169-5172
Number of pages4
ISBN (Electronic)978-1-5090-4951-6
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • computer vision
  • convolution
  • feature extraction
  • geophysical image processing
  • image classification
  • learning (artificial intelligence)
  • neural nets
  • CNNs outperform state
  • Dar es Salaam
  • VHR satellite images
  • convolutional neural networks
  • deep feature
  • different urban structure types
  • end-to-end fashion
  • high-resolution aerial
  • informal settlements
  • satellite imagery
  • spatial-contextual features
  • standard land-cover classes
  • Convolution
  • Feature extraction
  • Kernel
  • Machine learning
  • Support vector machines
  • Training
  • Image classification
  • deep learning
  • high resolution satellite imagery

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