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) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Hyperspectral image (HSI) provides rich spectral–spatial information and the light detection
and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for …

In this article, we propose an efficient and effective framework to fuse hyperspectral and light
detection and ranging (LiDAR) data using two coupled convolutional neural networks …

The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired
multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called …

Background Rapid and non-destructive infrared spectroscopy has been applied to both
internal and external quality evaluations of food and agro-products. Various linear and …

Deep learning methods have been widely used in hyperspectral image classification and
have achieved state-of-the-art performance. Nonetheless, the existing deep learning …