The recent advancements in security approaches have significantly increased the ability to
identify and mitigate any type of threat or attack in any network infrastructure, such as a …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …

Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …

The development of remote sensing images in recent years has made it possible to identify
materials in inaccessible environments and study natural materials on a large scale. But …

Few-shot learning considers the problem of learning unseen categories given only a few
labeled samples. As one of the most popular few-shot learning approaches, Prototypical …

Accurate and timely mapping of wildfire burned areas is crucial for post-fire management,
planning, and next subsequent actions. The monitoring and mapping of the burned area by …

Deep neural networks have achieved promising performance for hyperspectral image (HSI)
classification. However, due to the limitation of the available labeled samples, the traditional …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

Assessing the classification performance of ordinal classifiers is a challenging problem
under imbalanced data compositions. Considering the critical impact of the metrics on the …