NL-InSAR: Nonlocal Interferogram Estimation

Deledalle, Charles-Alban; Dénis, Loïc; Tupin, Florence

doi:10.1109/tgrs.2010.2076376

Cited by 246 publications

(212 citation statements)

References 38 publications

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“…Also, the pre-filtering of both intensity and coherence images was performed in an adaptive way using a nonlocal filter [55,56], which mostly conserves spatial resolution. Figure 11 illustrates the results obtained by applying the two techniques described in [53,54] to solve the problem of floods extraction from time-series SAR images.…”

Section: Comparaison With Existing Techniquesmentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

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Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.

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Section: Comparaison With Existing Techniquesmentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

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“…In particular, adaptive algorithms making use of amplitude statistics to fuse pixels with the same features are here analyzed. The algorithm in (Vasile et al, 2004), connecting pixels with a region growing technique, and the one in (Deledalle et al, 2011), connecting also not consecutive pixels inside a search window, are tested. The need to employ adaptive methods is clear looking at Fig.…”

Section: Interferogram Generation Stagementioning

confidence: 99%

“…6 is processed to obtain a mean theoretical resolution of 3.65 meters. The NL-InSAR algorithm (Deledalle et al, 2011) is used for the multilooking and the coherence estimation. The phase noise mitigation is evident comparing the two DEM portions.…”

Section: Interferogram Generation Stagementioning

confidence: 99%

Towards an Urban Dem Generation With Satellite Sar Interferometry

Rossi

Fritz

Eineder

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper analyzes the potentials offered by the TanDEM-X mission for the generation of urban DEMs. Operationally acquired urban Raw DEMs are validated using as reference a LiDAR DEM. The approach used for the validation is application-oriented as a subproduct is a derivation of a urban volume map. The mean buildings absolute height error is found to be below 5 meters. Neglecting the buildings geolocation errors, this value drops below 60 centimeters. Nevertheless, the operational municipal mapping is limited to the standard resolution of 12 meters. The limitation is attenuated with the use of high resolution spotlight data. High-resolution raw DEMs over Las Vegas and Berlin are first analyzed. Some modifications to the interferometric processing chain necessary to produce high quality DEMs are studied. The obtained results open a new perspective to the urban DEM generation with satellite SAR interferometry.

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“…The negation of their logarithm are given in Table 1. A more detailed description of the algorithm can be found in [8,11]. Non-local approaches are known to leave a high variance in regions where there are too few redundant patches.…”

Section: Setting Of the Weightsmentioning

confidence: 99%

A non-local approach for SAR and interferometric SAR denoising

Deledalle

Tupin

Denis

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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Recently, non-local approaches have proved very powerful for image denoising. Unlike local filters, the nonlocal (NL) means introduced in [1] decrease the noise while preserving well the resolution. In the proposed paper, we suggest the use of a non-local approach to estimate single-look SAR reflectivity images or to construct SAR interferograms. SAR interferogram construction refers to the joint estimation of the reflectivity, phase difference and coherence image from a pair of two co-registered single-look complex SAR images. The weighted maximum likelihood is introduced as a generalization of the weighted average performed in the NL means. We propose to set the weights according to the probability of similarity which provides an extension of the Euclidean distance used in the NL means. Experiments and results are presented to show the efficiency of the proposed approach.Index Terms-Non local means, maximum likelihood, interferometric synthetic aperture radar (InSAR)

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NL-InSAR: Nonlocal Interferogram Estimation

Cited by 246 publications

References 38 publications

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Towards an Urban Dem Generation With Satellite Sar Interferometry

A non-local approach for SAR and interferometric SAR denoising

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