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Radarcoding Reference Data for SAR Training Data Creation in Radar Coordinates
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AI
DOI：10.1109/LGRS.2024.3376992.png)
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Abstract
En 中文
Extracting training datasets for supervised classification of synthetic aperture radar (SAR) images is complicated, due to, e.g., poor radiometric resolution, speckle noise, and lack of reference data. It is challenging to link radar scatterers in SAR images with the counterparts in the reference datasets registered in geographic coordinate systems. To address this issue, this letter proposes a method called Rdr-Code to radarcode geodetic reference datasets for creating SAR training datasets for machine learning applications. To assess the importance of building heights in radarcoding, we compared the assignment of height values by a minuscule pseudo height with the actual building heights derived from a Lidar-based DEM product. We used 30 PAZ SAR images in X-band, which were acquired between 2019 and 2021, over the north-west part of the Netherlands, and employed Top10NL and AHN as reference LULC polygon and height datasets, respectively. The radarcoding accuracy was compared using nine buildings as references in the SAR coordinates. The radarcoding accuracy was 64.5% with the pseudo height and 84.5% with actual building heights. A trade-off between accurate building feature information and separation between close buildings was observed. We conclude that this is an effective way to radarcode reference datasets and can be used for crafting training datasets for machine learning methods.
Keywords:
Buildings
Synthetic aperture radar
Training
Radar polarimetry
Vectors
Radar imaging
Spatial resolution
AHN
radarcoding
supervised classification
synthetic aperture radar (SAR)
Top10NL
training data
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