Orthorectification of Sentinel-1 SAR (Synthetic Aperture Radar) Data in Some Parts Of South-eastern Sulawesi Using Sentinel-1 Toolbox

Bayanudin, Athar Abdurahman; Jatmiko, Retnadi Heru

doi:10.1088/1755-1315/47/1/012007

Cited by 19 publications

(12 citation statements)

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“…Geometric correction using the "Range-Doppler Terrain Correction (RDTC)" tool in the SNAP software was used for converting the Sentinel-1 GRD data from ground-range geometry into a map coordinate system [54]. In more detail, terrain correction was applied to transfer the single 2D of raster radar geometry to accurately geocode the images by correcting SAR geometric distortions using digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) (that allowed us to consider the local elevation variations) for geocoding Sentinel 1 imagery [55].…”

Section: Range-doppler Terrain Correctionmentioning

confidence: 99%

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SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

et al. 2020

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The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km2) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features.

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Section: Range-doppler Terrain Correctionmentioning

confidence: 99%

“…The areas without elevation were masked based on DEM data. Then, the process was running automatically after defining the parameters [54].…”

Section: Range-doppler Terrain Correctionmentioning

confidence: 99%

SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

et al. 2020

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“…These inaccuracies could explain the negative influence of the forest area variable in the global model (Table 2) as some relevant suitable areas for the bear were not correctly represented as forest in this model. Sentinel-1 allows mapping vegetation variables regardless of cloud conditions that limit optical imagery [64], but the potential issues derived from its use in hilly landscapes should be addressed [65].…”

Section: Habitat Suitability Modelsmentioning

confidence: 99%

The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

et al. 2021

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Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.

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“…The side-looking mechanism causes the difference of incidence angle from the near side to the far side, especially in large-width mode spaceborne SAR images [14][15][16]. Furthermore, after terrain correction, the azimuth and range directions are no longer consistent with the row and column directions in SAR orthophoto [15]. As a result, the quality of feature points and matching accuracy would be limited if the methods designed for optic image matching are directly applied to SAR [11,16].…”

Section: Introductionmentioning

confidence: 99%

“…However, since the SAR-SIFT lacks the controllability of feature number and spatial Unlike the optic image, SAR orthophoto has very different properties. The side-looking mechanism causes the difference of incidence angle from the near side to the far side, especially in large-width mode spaceborne SAR images [14][15][16]. Furthermore, after terrain correction, the azimuth and range directions are no longer consistent with the row and column directions in SAR orthophoto [15].…”

Section: Introductionmentioning

confidence: 99%

An Image Matching Method for SAR Orthophotos from Adjacent Orbits in Large Area Based on SAR-Moravec

et al. 2020

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In producing orthophoto mosaic in a large area from spaceborne synthetic aperture radar (SAR) images, SAR image matching from adjacent orbits is a technical difficulty due to the speckle noise and different imaging mechanism between azimuth and range direction. In this paper, an area-based method, SAR-Moravec, is proposed for SAR orthophoto matching from adjacent orbits in a large area. Compared with the classical area-based Moravec, the template of SAR-Moravec is characterized by more directions for speckle noise restraint and a main direction consistent with the azimuth. In order to get evenly distributed matching points with high accuracy, the grid control mechanism and Gaussian pyramid from coarse to fine are introduced in matching. The whole process contains three steps. First, the pyramid images are constructed by the down-sampling process. Second, under grid control, the feature points are evenly extracted by the modified template. Third, the transformation model is iteratively calculated from the first to the last layer of the pyramid. After the matching process layer-by-layer, the final matching points and transformation model can be obtained. In the experiments, we compare the SAR-Moravec with three widely used methods, including the Moravec, the SAR-scale invariant feature transform (SAR-SIFT), and the SAR-features from an accelerated segment test (SAR-FAST). The results indicate that the proposed method has the best global matching accuracy among these methods and the matching efficiency is better than SAR-SIFT and SAR-FAST methods in large area.

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Orthorectification of Sentinel-1 SAR (Synthetic Aperture Radar) Data in Some Parts Of South-eastern Sulawesi Using Sentinel-1 Toolbox

Cited by 19 publications

References 1 publication

SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

An Image Matching Method for SAR Orthophotos from Adjacent Orbits in Large Area Based on SAR-Moravec

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