Structure detection and statistical adaptive speckle filtering in SAR images

Lopès, A.; Nezry, E.; Touzi, R.; Laur, Henri

doi:10.1080/01431169308953999

Cited by 512 publications

(235 citation statements)

References 26 publications

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“…A 3ˆ3 window Gamma-MAP filter was used for speckle reduction. This adaptive filter is effective for reducing speckle while preserving linear characteristics and discrete scatterers [32]. The resulting processed images were resampled to a pixel size of 5 m. Finally, field medians were computed excluding border pixels by applying a 1-pixel buffer to the field boundary vector file.…”

Section: Image Processingmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

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Section: Image Processingmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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“…PALSAR SLC Level 1.1 images were multi-look processed to 4-looks [49] corresponding to 12.5 m pixel spacing (~70 × 70 km area coverage) using the SARScape "image processing workbench" module within ENVI [50,51]. These were then speckle filtered using a Gamma MAP filter [52] with a 5 × 5-pixel window, radiometrically calibrated and normalized by eliminating incidence angle effects and antenna gain and spread loss patterns. The radiometric normalization process used a modified cosine model [53].…”

Section: Palsarmentioning

confidence: 99%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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“…1. Before applying the model inversion using both images, we register the images to same size and geographic area, and filter both using a Gamma Maximum A Posteriori (MAP) filter with a kernel-size of 11×11 pixel (see [4]). This preprocessing is necessary to suppress the speckle noise that occurs in SAR images (see [5]).…”

Section: Resultsmentioning

confidence: 99%

Fast Derivation of Soil Surface Roughness Parameters Using Multi-band SAR Imagery and the Integral Equation Model

Seppke

Dreschler-Fischer

Heiming

et al. 2010

2010 20th International Conference on Pattern Recognition

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The Integral Equation Model (IEM) predicts the normalized radar cross section (NRCS) of dielectric surfaces given surface and radar parameters. To derive the surface parameters from the NRCS using the IEM, the model needs to be inverted. We present a fast method of this model inversion to derive soil surface roughness parameters from synthetic aperture radar (SAR) remote sensing data. The model inversion is based on two different collocated SAR images of different bands, the derivation of the parameters cannot be done using one band alone. The computation of the model and the model inversion are very time consuming tasks and therefore may be impractical for large remote sensing data. We present an approach that is based on a few model assumptions to speed up the computation of the surface parameters. We applied the algorithm to detect the correlation length of the surface for dry-fallen areas in the World Cultural Heritage "Wadden Sea", a coastal tidal flat at the German Bight (North Sea). The results are very promising and may be used for a classification of the area in future steps.

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Structure detection and statistical adaptive speckle filtering in SAR images

Cited by 512 publications

References 26 publications

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Fast Derivation of Soil Surface Roughness Parameters Using Multi-band SAR Imagery and the Integral Equation Model

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