SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research
state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery …

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …

Accurate crop type mapping provides numerous benefits for a deeper understanding of food
systems and yield prediction. Ever-increasing big data, easy access to high-resolution …

After the revival of deep learning in computer vision in 2012, SAR ship detection comes into
the deep learning era too. The deep learning-based computer vision algorithms can work in …

As the potential of foundation models in visual tasks has garnered significant attention,
pretraining these models before downstream tasks has become a crucial step. The three key …

Most of the existing synthetic aperture radar (SAR) ship instance segmentation models do
not achieve mask interaction or offer limited interaction performance. Besides, their …

Thanks to the excellent feature representation capabilities of neural networks, deep learning-
based methods perform far better than traditional methods on target detection tasks such as …

Existing instance segmentation models mostly pay less attention to the targeted
characteristics of ships in synthetic aperture radar (SAR) images, which hinders further …

Fine-scale land disturbances due to mining development modify the land surface cover and
have cumulative detrimental impacts on the environment. Understanding the distribution of …