@inproceedings {2330,
	title = {Recalibrating Sentinel-1 Additive Noise-Gain with Linear Programming},
	booktitle = {IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium},
	year = {2020},
	abstract = {<p>Synthetic aperture radar images from the Sentinel-1 program are obtained by interpreting signals from a non-uniform radiation pattern. Cross-polarized images in extra-wide mode show significant additive noise patterns that take the form of varying intensity and are independent of the ground targets. While Sentinel-1 provides a method for removing these noise patterns via noise-calibration files, there still remain significant issues in the transformed products, particularly from discontinuous changes among adjacent subswaths. In this work, we consider recalibration by assuming the noise-gain to be a power function of the platform\&$\#$39;s radiation pattern. We propose a method that estimates the scaling and exponent parameters of the power-function by applying linear programming to the log transform of the data and the radiation pattern intensity, alone with affine rescaling with least-squares estimation. Our method is able to rescale Sentinel-1 scenes to have a more consistent intensity profile among the subswaths of the image.</p>
},
	doi = {10.1109/IGARSS39084.2020.9324174},
	author = {Peter Q. Lee and Linlin Xu and David A. Clausi}
}
