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 …

Since the acquisition of digital images, scientific studies on these images have been making
significant progress. The sizes and quality of the images obtained have increased greatly …

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

In recent years, deep learning has been successfully applied to hyperspectral image
classification (HSI) problems, with several convolutional neural network (CNN) based …

Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …

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

Building change detection (BCD) for very-high-spatial-resolution (VHR) remote sensing
images is very important and challenging in the field of remote sensing, as the building is …

Due to its various application potentials, the remote sensing image scene classification
(RSSC) has attracted a broad range of interests. While the deep convolutional neural …

Recently, due to the powerful capability at modeling the long-range relationships,
Transformer-based methods have been widely explored in many research areas, including …

Convolutional neural networks (CNNs) have been extensively applied to hyperspectral (HS)
image classification tasks and achieved promising performance. However, for CNN-based …