Journal of Remote Sensing & GIS

Vision lab of the De´partement d’Informatique et de Recherche Ope´rationnelle (DIRO), Universite´ de Montre´al, Faculte´ des Arts et des Sciences, Montre´al, H3C 3J7, QC, Canada

Biography

Redha Touati received the M.Sc. degree in computer science from the Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, QC, Canada, in 2014 . He is currently working toward the Ph.D. degree at the Vision Laboratory of the University of Montreal (DIRO), in collaboration with the Imaging and Vision Department of the Computer Research Institute of Montreal (CRIM), Montreal, QC, USA.,His research interests include statistical methods and applied mathematics in video imaging and remote sensing imagery.

Publications

• Research Article   
  Partly Uncoupled Siamese Model for Change Detection from Heterogeneous Remote Sensing Imagery
  Author(s): Touati R*, Mignotte M and Dahmane M
  
  This paper addresses the problematic of detecting changes in bitemporal heterogeneous remote sensing image pairs. In different disciplines, multimodality is the key solution for performance enhancement in a collaborative sensing context. Particularly, in remote sensing imagery there is still a research gap to fill with the multiplication of sensors, along with data sharing capabilities, and multitemporal data availability. This study is aiming to explore the multimodality in a multi-temporal set-up for a better understanding of the collaborative sensor wide information completion; we propose a pairwise learning approach consisting on a pseudo-Siamese network architecture based on two partly uncoupled parallel network streams. Each stream represents itself a Convolutional Neural Network (CNN) that encodes the input patches. The overall Change Detector (CD) model in.. View more»
  

  DOI: 10.35248/2469-4134.20.9.271

  Abstract HTML PDF