Journal of Remote Sensing & GIS

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

Biography

Mignotte M is a research scientist and belongs to department of Montre´al, Faculte´ des Arts et des Sciences and is interested in the various fields of Heterogeneous Remote Sensing, Multi-sensor images, sciences, Spatial Analysis and Environmental Science.

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