With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

In recent years, enormous research has been made to improve the classification
performance of single-modal remote sensing (RS) data. However, with the ever-growing …

Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …

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

Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …