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