Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
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 …
particularly machine learning algorithms, range from initial image processing to high-level …
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image 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 …
Attention Networks (GAT), are two classic neural network models, which are applied to the …
Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification
Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
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 …
[HTML][HTML] A survey: Deep learning for hyperspectral image classification with few labeled samples
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) …
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
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 …
Topological structure and semantic information transfer network for cross-scene hyperspectral image classification
Domain adaptation techniques have been widely applied to the problem of cross-scene
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …
hyperspectral image (HSI) classification. Most existing methods use convolutional neural …
CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification
Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …
hyperspectral image (HSI) classification. Compared with the convolutional neural network …
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …