Poderico, Mariana (2011) Denoising of SAR images. [Tesi di dottorato] (Inedito)

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Denoising of SAR images
Autori:
AutoreEmail
Poderico, Marianamariana.poderico@unina.it
Data: 30 Novembre 2011
Numero di pagine: 95
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Ingegneria biomedica, elettronica e delle comunicazioni
Scuola di dottorato: Ingegneria dell'informazione
Dottorato: Ingegneria elettronica e delle telecomunicazioni
Ciclo di dottorato: 24
Coordinatore del Corso di dottorato:
nomeemail
Rinaldi, Niccolònirinaldi@unina.it
Tutor:
nomeemail
Verdoliva, Luisaverdoliv@unina.it
Poggi, Giovannipoggi@unina.it
Data: 30 Novembre 2011
Numero di pagine: 95
Parole chiave: Synthetic Aperture Radar (SAR); despeckling; linear minimum mean-square error (LMMSE) filtering; non-local filtering; empirical Wiener filtering; undecimated discrete wavelet transform (UDWT); performance assessment; objective measures; simulated SARAS images
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 - Telecomunicazioni
Depositato il: 07 Dic 2011 12:20
Ultima modifica: 30 Apr 2014 19:48
URI: http://www.fedoa.unina.it/id/eprint/8779
DOI: 10.6092/UNINA/FEDOA/8779

Abstract

Matter of discussion in this Ph. D. thesis is SAR (Synthetic Aperture Radar) image denoising. Main elements of innovation are the introduction of SAR-BM3D, a denoising algorithm optimized for SAR data, and the introduction of a benchmark which enables the objective performance comparison of SAR denoising algorithms on simulated canonical SAR images. In the first part of the thesis, basic concepts on SAR images are introduced, with special emphasis on its peculiar multiplicative noise, called speckle. A description of key ideas and tools of denoising techniques known in literature then follows. After introducing the basic elements of SAR data processing, the main statistical features of SAR images are described, and it is clarified in which context the denoising techniques operate. Techniques are then classified as those that follow the homomorphic approach, where the multiplicative noise is turned into additive noise through a logarithmic transform, and those that take explicitly into account the multiplicative nature of noise. Afterwards, it is described how the introduction of the wavelet transform has brought new ideas into SAR image denoising and how the non-local filtering strategy, originally proposed in the AWGN field, has provided relevant results also in the application to SAR. In this context, the novel SAR-BM3D algorithm is introduced which, starting from key elements of wavelet-based and non-local filtering implemented in BM3D, optimizes the elaboration for SAR data, following a non-homomorphic approach. A very detailed experimental analysis on simulated SAR images, obtained as optical images corrupted by artificial speckle, has been performed: results proved the SAR-BM3D algorithm to outperform traditional approaches, both in terms of PSNR and visual inspection. Due to the well-known difficulties of evaluating the performance of denoising techniques on real SAR images, a workaround has been proposed. Rather than resorting to images corrupted by artificial speckle, a physical SAR simulator, SARAS (developed by the remote-sensing group of the Federico II University of Naples) has been used to generate a set of canonical benchmark SAR scenes. The main advantage of SARAS images is the availability of both the noisy and clean versions of the images, the latter acting as a reference to objectively evaluate the performances of different algorithms. We have shown in detail the procedure which leads to a definition of an objective criterion to compare results provided by different algorithms when working on real SAR images. For this purpose, different test cases have been selected and specific measures, suitable for the various scenes have been proposed for the characterization. At the end of the thesis, open issues are pointed out and future research is outlined.

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