Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

The architecture, engineering and construction (AEC) industry is experiencing a
technological revolution driven by booming digitisation and automation. Advances in …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …

In this review, we present the main approaches developed around satellite and airborne
Synthetic Aperture Radar (SAR) imagery. The great range of SAR imagery applications is …

基于深度神经网络的雷达像智能识别技术已经成为雷达信息处理领域的前沿和热点. 然而,
神经网络分类模型易受到对抗攻击的威胁. 攻击者可以在隐蔽的条件下误导智能目标识别模型做 …

The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture
Radar (SAR) images that can penetrate cloud cover. However, the large domain gap …