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 …

The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

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 …

Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …