Long-time-series climate prediction is of great significance for mitigating disasters;
promoting ecological civilization; identifying climate change patterns and preventing floods …

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering
images of the illuminated area, playing a significant role in various applications. However …

In this paper, we propose an unsupervised image-to-image translation (UNIT) network as a
deep learning-based motion compensation method to compensate for phase errors that may …

ABSTRACT The Extreme Learning Machine (ELM) has sparked a lot of attention since it can
learn fast and be applied to various problems. In this study, a convolutional layer-based …

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing
electromagnetic images of the observation scene. The prior knowledge-based SAR image …

This letter proposes a complex-valued convolutional gated recurrent unit (CV-ConvGRU)
network for the 3-D rotation refocusing task of a synthetic aperture radar (SAR) ship target …

In a synthetic aperture radar (SAR) system, target rotation during the coherent integration
time results in a time-varying Doppler frequency shift and a blurred image. Blurred images …

A dual-channel airborne synthetic aperture radar (SAR) imaging approach is proposed to
acquire well-focused images of ground moving targets (GMTs) after compensating for the …

Autofocus plays a key role in synthetic aperture radar (SAR) imaging, especially for high-
resolution imaging. In the literature, the minimum-entropy-based algorithms (MEA) have …

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a
self-adapted model is a challenging problem. To address this issue, an efficient deep …