Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions
Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …
[HTML][HTML] Graph attention networks: a comprehensive review of methods and applications
Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …
effectively captured by graph learning systems. Graph attention networks (GATs) have …
Single-source domain expansion network for cross-scene hyperspectral image classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …
attention. It is necessary to train a model only on source domain (SD) and directly …
MFFCG–Multi feature fusion for hyperspectral image classification using graph attention network
Classification methods that are based on hyperspectral images (HSIs) are playing an
increasingly significant role in the processes of target detection, environmental …
increasingly significant role in the processes of target detection, environmental …
Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery
Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …
contribute to a more accurate identification of materials and land covers. However, most …
Attention multihop graph and multiscale convolutional fusion network for hyperspectral image classification
Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have
generated good progress. Meanwhile, graph convolutional networks (GCNs) have also …
generated good progress. Meanwhile, graph convolutional networks (GCNs) have also …
Multi-scale receptive fields: Graph attention neural network for hyperspectral image classification
Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …
Dual-view spectral and global spatial feature fusion network for hyperspectral image classification
For hyperspectral image (HSI) classification, two branch networks generally use
convolutional neural networks (CNNs) to extract the spatial features and long short-term …
convolutional neural networks (CNNs) to extract the spatial features and long short-term …
Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process
This article studies the performance monitoring problem for the potassium chloride flotation
process, which is a critical component of potassium fertilizer processing. To address its froth …
process, which is a critical component of potassium fertilizer processing. To address its froth …
Composite neighbor-aware convolutional metric networks for hyperspectral image classification
Supervised classification of hyperspectral image (HSI) is generally required to obtain better
performance in spectral–spatial feature learning by fully using complex pixel-and superpixel …
performance in spectral–spatial feature learning by fully using complex pixel-and superpixel …