A first comparison of Cosmo-SkyMed and TerraSAR-X data over Chamonix Mont-Blanc test-site

Nicolas, Jean‐Marie; Trouvé, Emmanuël; Fallourd, Renaud; Vernier, Flavien; Tupin, Florence; Harant, Olivier; Gay, Michel; Moreau, Luc

doi:10.1109/igarss.2012.6352053

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…This time series is composed of 26 single-look images of size 512 2 (azimuth resolution: 2 m, range resolution: 1 m) coregistered using the method provided in [52] based on the fine knowledge of the sensors parameters provided by TerraSAR-X. Thirteen of these images have been sensed in 2009, and 13 in 2011.…”

Section: B Application To Change Detectionmentioning

confidence: 99%

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Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

Lobry

Denis

Tupin

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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International audienceSpeckle phenomenon in synthetic aperture radar (SAR) images makes their visual and automatic interpretation a difficult task. To reduce strong fluctuations due to speckle, total variation (TV) regularization has been proposed by several authors to smooth out noise without blurring edges. A specificity of SAR images is the presence of strong scatterers having a radiometry several orders of magnitude larger than their surrounding region. These scatterers, especially present in urban areas, limit the effectiveness of TV regularization as they break the assumption of an image made of regions of constant radiometry. To overcome this limitation, we propose in this paper an image decomposition approach. There exists numerous methods to decompose an image into several components, notably to separate textural and geometrical information. These decomposition models are generally recast as energy minimization problems involving a different penalty term for each of the components. In this framework, we propose an energy suitable for the decomposition of SAR images into speckle, a smooth background and strong scatterers, and discuss its minimization using max-flow/min-cut algorithms. We make the connection between the minimization problem considered, involving the L0 pseudo-norm, and the generalized likelihood ratio test used in detection theory. The proposed decomposition jointly performs the detection of strong scatterers and the estimation of the background radiometry. Given the increasing availability of time series of SAR images, we consider the decomposition of a whole time series. New change detection methods can be based on the temporal analysis of the components obtained from our decomposition

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Section: B Application To Change Detectionmentioning

confidence: 99%

“…The decomposition of a 56-image (512 2 pixels each) stack of Paris acquired by TerraSar-X (spotlight mode, 1 m × 1 m) and coregistered using [52] is shown in Fig. 9.…”

Section: A Application To Regularizationmentioning

confidence: 99%

Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

Lobry

Denis

Tupin

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…After forming the ratio image between the noisy image and the "super-image", the proposed method takes advantage of the state-of-the-art single-image speckle reduction methods to denoise the ratio image [1]. We consider multi-temporal images acquired on the same orbit (i.e., either all ascending orbits, or all descending orbits), with similar incidence angles, and which have been accurately registered [27]. For the sake of reproducible research, we have released an open-source code at https://www.math.u-bordeaux.…”

Section: Introductionmentioning

confidence: 99%

Ratio-Based Multitemporal SAR Images Denoising: RABASAR

Zhao

Deledalle

Denis

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multi-temporal mean. The proposed approach can be divided into three steps: 1) estimation of a "super-image" by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the "super-image"; 3) computation of the denoised image by re-multiplying the denoised ratio by the "super-image". Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multi-temporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the "super-image" that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio, structure similarity index) as well as visually on simulated and SAR time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.

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“…Here, the consistency between the stripmap products of both missions is verified, but also the significant differences between the CSK and TSX polarization options is demonstrated. An image time series (ITS) based comparative analysis for glacier displacement using interferometry is carried out in [5]. Another comparative analysis of CSK and TSX satellite images in interferometric applications for monitoring of man-made structures at lagoon inlets is carried out in [6].…”

Section: Introductionmentioning

confidence: 99%

On the statistical similarity of synthetic aperture radar images from COSMO-SKYMED and TerraSAR-X

Singh

Espinoza-Molina

Schwarz

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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The latest generation of synthetic aperture radar (SAR) instruments operating in X-band, that is, COSMO-SkyMed (CSK) and TerraSAR-X (TSX), are capable of providing images from coarse resolution to very high resolution. A lot of research effort has been invested in the study and understanding of images obtained from these satellites. However, there is still a huge scope of statistical understanding and comparison of data from both satellites. In this study, we demonstrate some striking similarities between medium resolution data obtained from CSK and TSX Stripmap mode images. Landcover unsupervised clustering using k-means is discussed to further justify our findings. Clustering is carried out using a feature descriptor based on log-cumulants of Gabor coefficients, which was recently proposed by us in earlier studies.

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A first comparison of Cosmo-SkyMed and TerraSAR-X data over Chamonix Mont-Blanc test-site

Cited by 5 publications

References 7 publications

Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

Ratio-Based Multitemporal SAR Images Denoising: RABASAR

On the statistical similarity of synthetic aperture radar images from COSMO-SKYMED and TerraSAR-X

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