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 …

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

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 …

Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into
hyperspectral image classification (HSIC) and achieved impressive progress. To further …

The lack of labels is one of the major challenges in hyperspectral image (HSI) classification.
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …

In remotely sensed images, high intraclass variance and interclass similarity are ubiquitous
due to complex scenes and objects with multivariate features, making semantic …

Hyperspectral image (HSI) classification is an active research topic in remote sensing.
Supervised learning-based methods have been widely used in HSI classification tasks due …

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …