Synthetic aperture radar (SAR) remote sensing offers a number of advantages over optical remote sensing. SAR image acquisition is not limited to daytime. Unlike visible spectrum aerial images, SAR ...

Abstract: Automatically extracting buildings with high precision from remote sensing images is crucial for various applications. Due to their distinct imaging modalities and complementary ...

Abstract: The feature fusion of optical and Synthetic Aperture Radar (SAR) images is widely used for semantic segmentation of multimodal remote sensing images. It leverages information from two ...

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the ...

With the rapid advancements in oceanic remote sensing and deep learning, it is now possible to extract valuable insights from vast datasets. In this context, by building datasets using deep learning ...

SAR processors are designed as so-called end-to-end systems which generate highly precise calibrated products for scientists and commercial users from the bit data stream coming from SAR instruments.

We research Ku and L-band SAR remote sensing observations of snow, ice, and water for cryosphere and other water ... The CryoSAR is being used to explore L-band and Ku-band observations of lakes to ...

Synthetic aperture radar (SAR) is a type of remote sensing from satellites that uses ... coefficient of Sentinel-1 and optical Landsat images, researchers used a regression model and then expanded ...