2022/04/12 · We propose an adaptive cross-attention-driven spatial-spectral graph convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN) subnetwork, ...

Experiments on two HSI data sets show that the proposed method achieves better performance than other classification methods. Index Terms—Hyperspectral image ...

2022/04/12 · Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better ...

2022/04/12 · This work proposes an adaptive cross-attention-driven spatial–spectral graph convolutional network (ACSS-GCN), which is composed of a ...

含まれない: Classification. | 必須にする:Classification.

Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification · no code implementations • 12 Apr 2022 ...

Graph convolution operations enable information propagation between nodes, capturing complex associations and facilitating node classification. The GCN-based ...

Hyperspectral-high spatial resolution (H2) [1] images have finer spectral and spatial information [2] and can effectively distinguish spectrally similar objects ...

In this article, we propose a fast dynamic graph convolutional network and CNN (FDGC) parallel network for HSI classification. Paper

2024/05/30 · Very recently, Yang et al. [53] developed an adaptive graph cross-attention fusion module to suppress noise interference and to fuse the ...