Journal of Remote Sensing & GIS

R&D vision De´partement, Centre de Recherche Informatique de Montre´al (CRIM) Montre, Canada

Biography

Dahmane M is a associate professor and belongs to R&D vision Department at Centre de Recherche Informatique de Montre´al (CRIM) Montre and is interested in the various fields of Neural Networks and Artificial Intelligence, Pattern Recognition, Image Processing, Supervised Learning and Feature Extraction.

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

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