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 …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the
recent technological advancements and manufacturing cost reduction opened up myriad …

Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral
image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from …

Many learning-based algorithms have been developed to solve the inverse problem of
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate
construction materials and provide detailed geographic information. In remote sensing …

Scene classification is a highly useful task in Remote Sensing (RS) applications. Many
efforts have been made to improve the accuracy of RS scene classification. Scene …

Convolutional neural network (CNN)-based methods are widely used in remote sensing
image scene classification and can obtain excellent performances. However, the stacked …

Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …