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 …

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

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 …

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

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 …

Change detection (CD) is a hot research topic in the remote-sensing (RS) community. With
the increasing availability of high-resolution (HR) RS images, there is a growing demand for …

Transformer-based networks, which can well model the global characteristics of inputted
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …

Change detection (CD) is one of the important research topics in remote sensing (RS) image
processing. Recently, convolutional neural networks (CNNs) have dominated the RSCD …