Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and
other fields. However, existing detection methods have low accuracy due to the limitations …

Cropland resources are essential for the provision of food production, which is one of the
most fundamental needs of human life. Change detection (CD) technology enables the …

Building change detection (BCD) using high-resolution remote sensing images aims to
identify change areas during different time periods, which is a significant research focus in …

Spaceborne remote sensing via synthetic aperture radar (SAR) images offers promising
solutions to long-term structural health monitoring by providing local displacement time …

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from
remote sensing images. However, the intraclass heterogeneity of buildings is high in …

Building footprints provide essential information for mapping, disaster management, and
other large-scale studies. Synthetic Aperture Radar (SAR) provides consistent data …

Building change detection (BCD) is crucial for urban construction and planning. The
powerful discriminative ability of deep convolutions in deep learning-based BCD methods …

Building an oil spill segmentation model is very challenging because of the limited available
information on oil spill accidents. Therefore, this paper proposes a custom data generator …

In order to improve the accuracy of change detection, a novel synthetic aperture radar (SAR)
image change detection method based on Gamma correction and fuzzy local information c …

It is well known that there are geometric distortions in synthetic aperture radar (SAR) images
when the terrain undulates. Layover is the most common one, which brings challenges to …