Analysis and Evaluation of Speckle Filters by Using Polarimetric Synthetic Aperture Radar Data Through Local Statistics

Medasani, Sivasubramanyam; Reddy, G. Umamaheswara

doi:10.1109/iceca.2018.8474567

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…Sentinel-1 image pre-processing followed the workflow published in [66], which consisted of seven processing steps implemented through SNAP. The speckle noise reduction was conducted through the application of a Lee-Sigma filter [67]. Range Doppler terrain correction was made using the DEM produced in this study (for more details, consult [66][67][68].…”

Section: Sentinel-1 and Sentinel-2 Image Pre-processing Methodsmentioning

confidence: 99%

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Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

et al. 2022

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Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.

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Section: Sentinel-1 and Sentinel-2 Image Pre-processing Methodsmentioning

confidence: 99%

“…The speckle noise reduction was conducted through the application of a Lee-Sigma filter [67]. Range Doppler terrain correction was made using the DEM produced in this study (for more details, consult [66][67][68].…”

Section: Sentinel-1 and Sentinel-2 Image Pre-processing Methodsmentioning

confidence: 99%

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

et al. 2022

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“…There are several polarimetric speckle filters and each one has specific advantages. The comparative analysis of Farage et al (2007), Turkar (2010), Boutarfa et al (2015), Mousavi et al (2005) and Medasani & Reddy (2017) was reviewed. The Lee Refined Speckle Filter (Lee et al, 2006) was chosen, because in the comparative analyzes it proved to be useful for edge preservation (Mousavi et al, 2015;Medasani & Reddy, 2017) and to improve the accuracy of polarimetric classifications (Farage et al, 2007;Turkar, 2009).…”

Section: Polarimetric Speckle Filteringmentioning

confidence: 99%

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

G.¹,

U.²,

A.³

et al. 2020

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

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Abstract. Aquaculture fish cages are usually located remote areas with poor accessibility. Synthetic Aperture Radar (SAR) is relevant to register and monitor their activity. However, in order to take advantage of the large amount of information this technology produce, it is necessary to develop automated tools of analysis. This work proposes an automated approach through an unsupervised polarimetric classification method using Sentinel 1 IW SLC Dual-Pol (VV + VH) products. An experimental evaluation was applied in Calbuco, an area with intense aquaculture activity in southern Chile. It was possible to demonstrate that this approach allows to improve the capacity of classification of previous experiences (86.49% user’s accuracy and 96.97% producer´s accuracy). Further studies are required to know the impact of wind speed on the classification as well as the spatial precision of the detection.

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“…This kernel size is suitable given the size of the fields in this region of Canada. Furthermore, this filter type was found appropriate in preserving the phase information of the SAR imagery [42]. Derived HH and VV intensity products were geometrically corrected and geo-referenced using the range-Doppler terrain correction method [43].…”

Section: In-situ Datamentioning

confidence: 99%

Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

et al. 2019

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Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m3/m3. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m3/m3). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

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Analysis and Evaluation of Speckle Filters by Using Polarimetric Synthetic Aperture Radar Data Through Local Statistics

Cited by 7 publications

References 11 publications

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

AUTOMATED FISH CAGES INVENTORYNG AND MONITORING USING H/a/Α UNSUPERVISED WISHART CLASSIFICATION IN SENTINEL 1 DUAL POLARIZATION DATA

Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

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