Change detection in unlabeled optical remote sensing data using siamese CNN

Rachid Hedjam*, Abdelhamid Abdesselam, Farid Melgani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this article, we propose a new semisupervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese convolutional neural network (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input images. The main contribution of this work lies in developing an S-CNN training phase without resorting to class labels that are actually not available from the input images. We train the S-CNN using genuine and impostor patch-pairs defined in a semisupervised way from the input images. We tested the proposed change detection model on four real datasets and compared its performance to those of two existing models. The obtained results were very promising.

Original languageEnglish
Article number9140297
Pages (from-to)4178-4187
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication statusPublished - 2020


  • Remote sensing change detection (CD)
  • Siamese convolutional neural network (CNN)
  • semisupervised CD

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science


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