Within the next several years, there will be a high level of autonomous technology that will
be available for widespread use, which will reduce labor costs, increase safety, save energy …

Ship detection and classification pose significant challenges in remote sensing. The potent
feature extraction capabilities of deep learning algorithms render them pivotal for these …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Object detection in optical remote-sensing images is an important and challenging task. In
recent years, the methods based on convolutional neural networks (CNNs) have made good …

Recently few-shot object detection (FSOD) in remote sensing images (RSIs) has drawn
increasing attention. However, the current FSOD methods in RSIs merely focus on the …

Road surface extraction from remote sensing images using deep learning methods has
achieved good performance, while most of the existing methods are based on fully …

Object detection in aerial images has received extensive attention in recent years. The
current mainstream anchor-based methods directly divide the training samples into positives …

Optical remote sensing object detection is a challenging task, because of the complex
background interference, ambiguous appearances of tiny objects, densely arranged …

Fine-grained image classification can be considered as a discriminative learning process
where images of different subclasses are separated from each other while the same …

Ship detection is a crucial but challenging task in optical remote sensing images. Recently,
thanks to the emergence of deep neural networks (DNNs), significant progress has been …