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Found 8 papers, 8 papers with code
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery
Specifically, the DGSSC comprises three components, a two-stage encoder, a decoder, and a classifier, which are trained in an end-to-end manner.
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive.
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC.
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification.
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
