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

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

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 …

With the development of space-based imaging technology, a larger and larger number of
images with different modalities and resolutions are available. The optical images reflect the …

Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …

Extracting building footprints from remotely sensed imagery has long been a challenging
task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes …

Supervised machine learning and deep learning methods perform well in hyperspectral
image classification. However, hyperspectral images have few labeled samples, which …

With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …

Remote sensing images contain a wealth of spatial information. Efficient scene classification
is a necessary precedent step for further application. Despite the great practical value, the …

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