Simultaneous Registration, Segmentation and Change Detection from Multisensor, Multitemporal Satellite Image Pairs

Vakalopoulou M., Platias C., Papadomanolaki M., Paragios N*., Karantzalos K.

NTUA, Greece

* ECP, France
Corresponding authors: &

A novel generic graph-based framework was developed addressing simultaneous registration, segmentation and change detection in multisensor data of different spectral, spatial and temporal resolutions. The quite promising experimental results indicated: (i) for the registration task a mean displacement error of less than 2 pixels, (ii) for the segmentation task in most cases completeness and correctness rates above 77% and (iii) for the change detection around or above 70%. The main errors derived from the important relief displacements around the tallest buildings and from the quite similar reflectance spectra of the different man-made objects including ship/vessels.


Registration Results


Segmentation Results


Change Detection Results



The developed framework can take as an input a pair of multisensor images (as the raw unregistered ones below) and perfoms simultaneously a co-registration, co-segmentation and change detection.

Those three problems are adressed concurrently from a single energy formulation (Eq.5), while classification scores are also exploited (Eq.2 & Eq.4) within, based on a deep learning & CNN procedure (classification maps below).



Raw Unregistered Data


VHR Deimos-2 | March 2015


VHR Deimos-2 | May 2015

@ 70cm, 4 spectral bands   @ 70cm, 4 spectral bands


Google Image Mosaic & Google Map


Iris Satellite Video Sequence | July 2015

@ level 17, RGB   @ 1m, RGB (first frame)







Deep-learning & CNN

The developed framework exploits classification scores derived from a deep-learning procedure


Deimos-2 (May'15) - Classification Map

Deep-learning & CNN with 8 classes





Deimos-2 (March'15) - Classification Map

Deep-learning & CNN with 8 classes




Iris Video (July'15) - Classification Map

Deep-learning & CNN with 7 classes