Forcing scale invariance in multipolarization SAR change detection
Carotenuto, Vincenzo and De Maio, Antonio and Clemente, Carmine and Soraghan, John J. and Alfano, Giusi (2016) Forcing scale invariance in multipolarization SAR change detection. IEEE Transactions on Geoscience and Remote Sensing, 54 (1). pp. 36-50. ISSN 0196-2892 (https://doi.org/10.1109/TGRS.2015.2449332)
Preview |
Text.
Filename: Carotenuto_etal_IEEE_TOGRS_2015_Forcing_scale_invariance_in_multi_polarization.pdf
Final Published Version License: Download (2MB)| Preview |
Abstract
This paper considers the problem of coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multi-polarization SAR change detec- tion starting from the availability of image pairs exhibiting possible power mismatches/miscalibrations. The principle of invariance is used to characterize the class of scale-invariant decision rules which are insensitive to power mismatches and ensure the Constant False Alarm Rate (CFAR) property. A maximal invariant statistic is derived together with the induced maximal invariant in the parameter space which significantly compress the data/parameters domain. A Generalized Likelihood Ratio Test (GLRT) is synthesized both for the cases of two- and three-polarimetric channels. Interestingly, for the two-channel case, it is based on the comparison of the condition number of a data-dependent matrix with a suitable threshold. Some additional invariant decision rules are also proposed. The performance of the considered scale-invariant structures is compared to those from two non- invariant counterparts using both simulated and real radar data. The results highlight the robustness of the proposed method and the performance tradeoff involved
ORCID iDs
Carotenuto, Vincenzo, De Maio, Antonio, Clemente, Carmine ORCID: https://orcid.org/0000-0002-6665-693X, Soraghan, John J. ORCID: https://orcid.org/0000-0003-4418-7391 and Alfano, Giusi;-
-
Item type: Article ID code: 53481 Dates: DateEvent31 January 2016Published19 August 2015Published Online10 June 2015AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 25 Jun 2015 13:47 Last modified: 19 Dec 2024 01:16 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/53481