Due to the complementary nature of optical and synthetic aperture radar (SAR) images, their
alignment is of increasing interest. However, due to the significant radiometric differences …

In recent years, deep neural networks have significantly boosted the performance of
polarimetric synthetic aperture radar (PolSAR) image classification. However, existing deep …

Lack of labeled data is a common problem among synthetic aperture radar (SAR) target
recognition, which can be defined as few-shot and limited-data SAR target recognition. The …

Semantic change detection (SCD) is a challenging task in remote sensing image (RSI)
interpretation, which adopts multitemporal images to detect, locate, and analyze pixel-level …

Matching multimodal remote sensing images (RSIs) remains an ongoing challenge due to
the significant nonlinear radiometric differences and geometric distortions, resulting in …

The gradual increase of synthetic aperture radar (SAR) imagery often accompanies the
appearance of new targets, but traditional detection frameworks can only detect existing …

Remote sensing image registration is one of the crucial steps in remote sensing image
processing, where ground control information is essential. Maintenance of control point …

Synthetic aperture radar (SAR) image target detection methods based on semisupervised
learning, such as the mean teacher framework, have shown promise in diminishing the …

Recently, deep learning methods have been widely adopted for ship detection in synthetic
aperture radar (SAR) images. However, many of the existing methods miss adjacent ship …

Synthetic Aperture Radar (SAR) and optical image registration is essential for remote
sensing data fusion, with applications in military reconnaissance, environmental monitoring …