Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

As one of the important components of Earth observation technology, land use and land
cover (LULC) image classification plays an essential role. It uses remote sensing techniques …

Few-shot learning is an important and challenging research topic for remote sensing image
scene classification. Many existing approaches address this challenge by using meta …

Few-shot scene classification aims to recognize task data, which consists of unlabeled data
and a few annotated samples, given some labeled auxiliary data. The task and the auxiliary …

Vision transformer (ViT) models have recently emerged as powerful and versatile tools for
various visual tasks. In this article, we investigate ViT in a more challenging scenario within …

The central goal of few-shot scene classification is to learn a model that can generalize well
to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent …

Multispectral (MS) pan-sharpening aims at producing high-resolution (HR) MS images in
both spatial and spectral domains, by merging single-band panchromatic (PAN) images and …

Multispectral (MS) pansharpening aims to improve the spatial resolution of MS images
(MSIs) using the spatial details of panchromatic (PAN) images. Due to the gap of prior …

Accuracy anomaly detection in user-level social multimedia traffic is crucial for privacy
security. Compared with existing models that passively detect specific anomaly classes with …

Despite the rapid advance in multispectral (MS) pansharpening, existing convolutional
neural network (CNN)-based methods require training on separate CNNs for different …