Paper Info
Title
A multiscale contextual approach to change detection in multisensor VHR remote sensing images
Abstract
The problem of unsupervised change detection from multisensor very high resolution images is addressed in this paper by focusing on the case in which multitemporal SAR data but only a single-date optical observation are available. This peculiar and challenging scenario is especially interesting in disaster management applications in which SAR acquisitions are feasible both before and after the event and an optical image is available only at one date (e.g., from the archive). The proposed method combines a novel Markov random field model with multiscale region-based analysis in order to fuse the information associated both with the statistics of the ratio of the multitemporal SAR images and with the spatial-geometrical structure of the observed scene captured by the optical image. Parameter estimation is based on a dictionary of parametric families and is carried out through the expectation-maximization algorithm and the method of log-cumulants. Graph cuts are used to minimize the energy function of the proposed MRF model. Experimental results are presented with COSMO-SkyMed and GeoEye-1 images.
Year
DOI
Venue
2013
10.1109/IGARSS.2013.6723567
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
Markov processes,emergency management,expectation-maximisation algorithm,geophysical image processing,image fusion,parameter estimation,remote sensing by radar,synthetic aperture radar,COSMO-SkyMed,GeoEye-1 images,MRF model,Markov random field model,SAR acquisitions,disaster management applications,expectation-maximization algorithm,graph cuts,log-cumulants,multiscale contextual approach,multiscale region-based analysis,multisensor VHR remote sensing images,multisensor very high resolution images,multitemporal SAR data,multitemporal SAR images,optical image,parameter estimation,parametric families,single-date optical observation,spatial-geometrical structure,unsupervised change detection,Markov random fields,Unsupervised change detection,graph cuts,multiscale segmentation,multisensor data fusion,region-based analysis
Cut,Change detection,Image fusion,Computer science,Synthetic aperture radar,Markov random field,Remote sensing,Image segmentation,Artificial intelligence,Computer vision,Pattern recognition,Parametric statistics,Image resolution
Conference
ISSN
ISBN
Citations 
2153-6996
978-1-4799-1114-1
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Gabriele Moser191976.92
Michaela De Martino2144.28
Serpico, S.B.356048.52