The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

Deforestation has become a major cause of climate change, and as a result, both
characterizing the drivers and estimating segmentation maps of deforestation have piqued …

Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …

Classification methods that are based on hyperspectral images (HSIs) are playing an
increasingly significant role in the processes of target detection, environmental …

In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …

Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …

Deep learning has certainly become the dominant trend in hyperspectral (HS) remote
sensing (RS) image classification owing to its excellent capabilities to extract highly …

In recent years, deep-learning-based hyperspectral image (HSI) classification networks
have become one of the most dominant implementations in HSI classification tasks. Among …

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …

The highly correlated spectral features and the limited training samples pose challenges in
hyperspectral image classification. In this article, to tackle the issues of end-to-end feature …