Nonlocal Multiscale Single Image Statistics From Sentinel-1 SAR Data for High Resolution Bitemporal Forest Wind Damage Detection

Change detection of synthetic aperture radar (SAR) data is a challenge for high-resolution applications. This study presents a new nonlocal averaging approach (STAl'SAR) to reduce the speckle of single Sentinel-1 SAR images and statistical parameters derived from the image. The similarity of SAR pixels is based on the statistics of 3 x 3 window as represented by the mean, standard deviation, median, minimum, and maximum. K-means clustering is used to divide the SAR image in 30 similarity clusters. The nonlocal averaging is carried out within each cluster separately in magnitude order of the 3 x 3 window averages. The nonlocal filtering is applicable not only to the original pixel backscattering values but also to statistical parameters, such as standard deviation. The statistical parameters to be filtered can represent any window size, according to the need of the application. The nonlocally averaged standard deviation derived in two spatial resolutions, 3 x 3 and 7 x 7 windows, are demonstrated here for improving the resolution in which the forest damages can be detected using the VH polarized backscattering spatial variation change.Peer reviewe

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

SAR Image Despeckling by Soft Classification

We propose a new approach to synthetic aperture radar (SAR) despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixelwise characterization of the image, one can take advantage of this diversity by selecting the most appropriate combination of estimators for each image region. Following this paradigm, we develop a simple algorithm where only two state-of-The-Art despeckling tools, characterized by complementary properties, are linearly combined. To ensure the smooth combination of contributes, thus avoiding new artifacts, we propose a novel soft classification method, where a basic estimate of homogeneity is improved through nonlocal and multiresolution processing steps. The results of a number of experiments conducted on both synthetic and real-world SAR images are very promising, thus confirming the potential of the proposed approach