Filtering of multichannel SAR images

Quegan, S.; Yu, Jihong

doi:10.1109/36.964973

Cited by 229 publications

(151 citation statements)

References 6 publications

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“…Due to the high accuracy of the S1 orbit data, geolocation at sub-pixel accuracy is achieved [20]. In the next step, the speckle-related uncertainty is reduced by applying a multichannel SAR intensity filter [31]. This filter optimally reduces speckle while preserving the intensity and spatial resolution of the individual channels.…”

Section: Classification Algorithmmentioning

confidence: 99%

“…This filter optimally reduces speckle while preserving the intensity and spatial resolution of the individual channels. In the filtering process, M new images J k are produced by linearly combining M co-registered intensity images I i , with 1 ď i ď M. For intensity images with the same number of looks, L, the following formulation for the filter is valid [31]:…”

Section: Classification Algorithmmentioning

confidence: 99%

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Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

et al. 2016

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Abstract:The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in environment, climate and security. We developed, tested and evaluated an algorithm for generating maps of snowmelt area from C-band synthetic aperture radar (SAR) data of the Sentinel-1 mission. For snowmelt classification, a change detection method is applied, using multitemporal dual-polarized SAR data acquired in Interferometric Wide swath (IW) mode, the basic operation mode over land surfaces. Of particular benefit for wet snow retrievals are the high instrument stability, the high spatial resolution across the 250 km wide swath, and the short revisit time. In order to study the impact of polarization, we generated maps of melting snow using data of the VV-polarized channel, the VH-polarized channel and a combined VV-and VH-based channel using a weighting function that accounts for effects of the local incidence angle. Comparisons are performed with snow maps derived from Landsat images over study areas in the Alps and in Iceland. The pixel-by-pixel comparisons show good agreement between the snow products of the two sensors, with the best performance for retrievals based on the combined (VV and VH) channel and a minor decline for the VH-based product. The VV-based snowmelt extent product shows a drop-off in quality over areas with steep terrain because of the decreasing backscatter contrast of snow-covered versus snow-free surfaces on fore-slopes. The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas.

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Section: Classification Algorithmmentioning

confidence: 99%

Section: Classification Algorithmmentioning

confidence: 99%

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

et al. 2016

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“…To achieve better stability of the estimation, we used a multitemporal aggregation technique similar to those described in [63,64]. In the latter, Quegan and Yu have suggested a method for speckle filtering of SAR imagery by combining several channels or scenes.…”

Section: Multi-scene Aggregationmentioning

confidence: 99%

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

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Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m 3 /ha) for scene acquired in frozen conditions. An RMSE of 40.8 m 3 /ha (42.9%, R 2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m 3 /ha (35.8%, R 2 = 0.77) compared to 38.8-51.6 m 3 /ha (40.8-54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m 3 /ha (34%, R 2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.

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“…By subtracting the HV and HH backscatter time series, an HVHH backscatter ratio time series was derived (HVHH). In addition to the general SAR pre-processing steps, adaptive multi-temporal SAR filtering [69] was applied to the time series stacks. The multi-temporal filtered backscatter time series are hereafter referred to as HHmt, HVmt and HVHHmt.…”

Section: Landsat Ndvi Datamentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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Filtering of multichannel SAR images

Cited by 229 publications

References 6 publications

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

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