Jan 23, 2020 · A novel semisupervised classification framework based on graph attention networks (GATs) for HSIs is proposed.

A novel semisupervised network based on graph sample and aggregate-attention (SAGE-A) for HSIs' classification is proposed, which uses the graph attention ...

In the convolution process, different weights are assigned to different neighboring nodes according to their attention coefficients, avoiding designing.

Graph based semi-supervised learning provides an effective solution to model data in classification problems, of which graph construction is the critical step.

Dec 12, 2023 · We propose a novel semisupervised algorithm for HSI classification by introducing spectral angle distance (SAD) as a loss function and employing multilayer ...

Dec 20, 2020 · We propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node ...

Jul 14, 2024 · In this paper, we introduce a semi-supervised classification method based on multi-scale spectral-spatial graph attention network (MSSGAT).

In this paper, we proposed a semisupervised classification algorithm based on multi-decision labeling and deep feature learning for hyperspectral image ...

Semisupervised Classification for Hyperspectral Images Using Graph Attention Networks · IEEE Geoscience and Remote Sensing Letters 18(1): 157-161 · 2021 · Related ...

Mar 20, 2024 · We propose a new end-to-end hyperspectral image classification model combining 3D–2D hybrid convolution and a graph attention mechanism (3D–2D-GAT).