Fully Convolutional Siamese Networks for Change Detection

Published in IEEE International Conference on Image Processing, 2018

Authors: R. Caye Daudt, B. Le Saux, A. Boulch


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This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.


  TITLE = { {Fully Convolutional Siamese Networks for Change Detection} },
  AUTHOR = {Boulch, Alexandre and Daudt, Rodrigo Caye and Le Saux, Bertrand},
  URL = {https://hal.archives-ouvertes.fr/hal-01824557},
  BOOKTITLE = { {IEEE International Conference on Image Processing} },
  ADDRESS = {Athens, Greece},
  YEAR = {2018},
  MONTH = Oct,
  KEYWORDS = {Index Terms- Change detection ; supervised machine learning ; fully convolutional networks ; Earth observation},
  PDF = {https://hal.archives-ouvertes.fr/hal-01824557/file/2018_icip_change.pdf},
  HAL_ID = {hal-01824557},
  HAL_VERSION = {v1},