Graph representation learning meets computer vision: A survey
A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …
information about individuals but also capture the interactions between individuals for …
Hyperspectral and LiDAR data classification based on structural optimization transmission
With the development of the sensor technology, complementary data of different sources can
be easily obtained for various applications. Despite the availability of adequate multisource …
be easily obtained for various applications. Despite the availability of adequate multisource …
Graph convolutional networks for hyperspectral image classification
Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …
[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) …
Anomaly detection on attributed networks via contrastive self-supervised learning
Anomaly detection on attributed networks attracts considerable research interests due to
wide applications of attributed networks in modeling a wide range of complex systems …
wide applications of attributed networks in modeling a wide range of complex systems …
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 …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
A semisupervised Siamese network for hyperspectral image classification
With the development of hyperspectral imaging technology, hyperspectral images (HSIs)
have become important when analyzing the class of ground objects. In recent years …
have become important when analyzing the class of ground objects. In recent years …
EMS-GCN: An end-to-end mixhop superpixel-based graph convolutional network for hyperspectral image classification
The lack of labels is one of the major challenges in hyperspectral image (HSI) classification.
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …