Automatic computation of speckle standard deviation in SAR images

Frulla, L.A.; Milovich, J.; Gagliardini, Domingo A.

doi:10.1080/01431160050121294

Cited by 8 publications

(4 citation statements)

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“…Since the value of the noise variance is an essential part of SDC, it should first be estimated from the image. Different solutions to the noise variance estimation problem in image had been published, such as the autoregressive model [26], the least-squares [27] and the wavelet decomposition [28].…”

Section: Resultsmentioning

confidence: 99%

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Speckle noise filtering based on signal subspace technique

Yahya

Kamel

Malik

2012

2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom)

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In this paper, a subspace-based technique for speckle noise reduction in images is proposed. The method is based on a linear model obtained by minimizing the energy of image distortion while keeping the energy of the residual noise in each spectral component below some given threshold. Image enhancement is achieved by removing the noise subspace and estimating the clean signal from the remaining signal subspace. The performance of the proposed approach is tested with simulated images and real SAR images, and compared with Lee and homomorphic anisotropic diffusion filters. The results indicate that the proposed technique increases the signal-to-noise ratio (SNR) by 3.6dB to 5.7dB over the noisy image. In addition, the proposed SDC algorithm is better at preserving the fine texture and introduces less blurring effect into the denoised image.

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Section: Resultsmentioning

confidence: 99%

“…The spectral-domain constrained subspace method is implemented using spectral constraint α i given in (27). The implementation steps are as follows, 1) Apply the homomorphic transformation to the noisy image, G = W N. …”

Section: Implementation Of Signal Subspace Approach For Multiplicamentioning

confidence: 99%

Speckle noise filtering based on signal subspace technique

Yahya

Kamel

Malik

2012

2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom)

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“…We adopt an automatic procedure to estimate speckle standard deviation from SAR images as in Frulla et al [24], which observed that the CV should be equal to the theoretical speckle standard deviation according to the number of looks of the SAR image for fully developed speckled images. However, the coefficient of variation measured over homogeneous regions on the images may differ from theoretical values due to several reasons such as the inherent image variations and the size of the selected sample.…”

Section: Speckle Statistics and Image Modelmentioning

confidence: 99%

SAR imagery segmentation by statistical region growing and hierarchical merging

Carvalho

Ushizima

Medeiros

et al. 2010

Digital Signal Processing

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This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step oversegments the input image to enable region aggregation by employing a combination of the KolmogorovSmirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for the process coordination. We have tested and assessed the proposed technique on artificially speckled image and real SAR data containing different types of targets 1 .

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“…Therefore we decided to implement Lee's polarimetric filter [5]. The speckle standard deviation is estimated according to Frulla [4]. to achieve some independency from the data and assist the less experienced user.…”

Section: Hv Absmentioning

confidence: 99%

OSCAR-object oriented segmentation and classification of advanced radar allow automated information extraction

Benz¹,

Baatz²,

Schreier³

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing

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DEFiNiENS is a new innovation oriented German company which developed a powerful image analysis software, eCognition. One of the cu rrent research projects within the company is to expand the capabilities of eCognition for SAR information extraction. These developments will help remote sensing on its way from a research oriented to an operational technology with commercial benefit. To support this development, the German Aerospace Center (DLR) co-funds the project ProSmart II and therein the sub-project OSCAR (object oriented segmentation and classification of advanced SAR data). In OSCAR, DEFiNiENS improves the suitability of eCognition for automatic information extraction and validates the results together with ProSmart II partners on exemplary applications. OSCAR IN PROSMART IIThe possibilities of remote sensing for many applications become visible more and more for "scanning the present and resolving the future" for scientific approaches. However, automatic information extraction for operational use is still in the very beginning. This is important to increase the public awareness for the advantages of remote sensing.Three main requirements have to be fulfilled for operational remote sensing: 1) reliable, periodical data delivery of sufficient and constant quality. Due to atmospheric disturbances this is hardly possible with optical data. Commercial entities such as infoterra -an Asrtium company -have therefore decided to develop a SAR satellite, TerraSAR, which should be launched in 2005; 2) delivery of appropriate information instead of a huge amount of data. For commercial use excellent quality of information extraction has to be maintained, and automatic methods have to be applied to allow sufficient throughput and to decrease information costs for the customer. 3) fusion of data and information, because no sensor data alone will be sufficient to achieve the required information quality for all considered applications.ProSmart II started in October 2000 and lasts until April 2002. It is a project to prepare operational product generation from TerraSAR data. Suitable information products have to be identified, requirements for data and algorithms specified and reliable and stable methods for information extraction developed. Precision farming, forestry and urban mapping with focus on change detection have been identified as important fields for demonstration projects. DLR's airborne SAR, E-SAR delivers polarimetric SAR data in high resolution and quality and in simulated TerraSAR quality. Those project partners, who are responsible for application development, map ground measurements to the data. Several comp anies including DEFiNiENS and universities work together as innovation and technology partners. They adapt segmentation algorithms and automatic classification to SAR, consider overall automation of the process, investigate possible synergy of the methods, and study embedding of all the developed algorithms in an upgraded ERDAS IMAGINE or prepare close interaction with ERDAS environment.Technology dev...

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Automatic computation of speckle standard deviation in SAR images

Cited by 8 publications

References 0 publications

Speckle noise filtering based on signal subspace technique

Speckle noise filtering based on signal subspace technique

SAR imagery segmentation by statistical region growing and hierarchical merging

OSCAR-object oriented segmentation and classification of advanced radar allow automated information extraction

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