Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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 …

In this paper, we rethink the low-light image enhancement task and propose a physically
explainable and generative diffusion model for low-light image enhancement, termed as Diff …

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 …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

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

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

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …