Name
Abstract | ||
|---|---|---|
This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351111 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geophysical image processing,nonparametric statistics,radar imaging,remote sensing by radar,speckle,statistical distributions,synthetic aperture radar,COSMO-SkyMed satellite constellation,MS algorithm,PDF,bivariate distribution,change detection,image acquisition,image pixel,log ratio,mean shift algorithm,multitemporal SAR image,probability density function,robust unsupervised nonparametric method,scatterplot,speckle pattern,synthetic aperture radar,Change detection,information-theoretic features,mean shift algorithm,multi-temporal images,non-parametric methods,synthetic aperture radar (SAR) | Change detection,Speckle pattern,Computer science,Synthetic aperture radar,Remote sensing,Probability distribution,Artificial intelligence,Computer vision,Radar imaging,Joint probability distribution,Pattern recognition,Pixel,Mean-shift | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 3 |
PageRank | References | Authors |
0.49 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Garzelli | 1 | 574 | 41.36 |
| Zoppetti, C. | 2 | 3 | 0.49 |
| B. Aiazzi | 3 | 528 | 49.43 |
| Stefano Baronti | 4 | 559 | 50.87 |