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 images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …

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

In this paper, we propose a remote-sensing scene-classification method based on vision
transformers. These types of networks, which are now recognized as state-of-the-art models …

Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …

The task of hyperspectral image (HSI) classification is fundamental and crucial in HSI
processing. Currently, domain adaptive methods have become a research hotspot in HSI …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …

Due to the unique feature of the three-dimensional convolution neural network, it is used in
image classification. There are some problems such as noise, lack of labeled samples, the …

Convolutional neural networks (CNN) have led to a successful breakthrough for
hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of …