NDLI: A multiscale contextual approach to change detection in multisensor VHR remote sensing images

Content Provider	IEEE Xplore Digital Library
Author	Moser, G. De Martino, M. Serpico, S.B.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr., Electron., Telecommun. Eng. & Naval Archit. (DITEN), Univ. of Genoa, Genoa, Italy (Moser, G.; De Martino, M.; Serpico, S.B.)
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.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	3435
Ending Page	3438
File Size	296907
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723567
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optical imaging Image segmentation Synthetic aperture radar Optical sensors Remote sensing Image resolution Image edge detection multiscale segmentation Unsupervised change detection multisensor data fusion Markov random fields graph cuts region-based analysis
Content Type	Text
Resource Type	Article

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