.png){kind=icon}
返回
Separable Deep Graph Convolutional Network Integrated With CNN and Prototype Learning for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3390575.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph convolutional networks (GCNs) have garnered extensive attention in the realm of hyperspectral image (HSI) classification. However, due to the problem of oversmoothing caused by deep GCN, most of the existing GCN-based methods are limited to constructing shallow networks, thus only able to extract superficial features. Moreover, when existing shallow GCNs extend to a more deeper structure, the number of learnable parameters increases linearly, thus leading to poor generalization performance under limited training samples. To address the aforementioned issues, a separable deep GCN integrated with convolutional neural network and prototype learning (SDGCP) is proposed for HSI classification, which can extract effective global structural information of HSI without increasing the number of trainable parameters. Specifically, the spectral and spatial features, adaptively selected by the attention module, are encoded into the structure of a graph by the graph encoder with the assistance of the pixel-to-region mapping obtained from the simple linear iterative clustering (SLIC). Then, a separable deep graph convolution module, composed of feature extraction and deep feature propagation, is adopted to capture the long-range contextual relationships from HSI encoded as graph data, which is combined with locally complementary information extracted by CNN after decoding. Finally, to further boost the performance of classification under limited labeled samples, prototype learning with regularization terms is utilized to enhance the intraclass compactness and interclass separability of feature representations. Extensive experiments on three standard HSI datasets demonstrate the superiority of the proposed SDGCP over the state-of-the-art (SOTA) methods.
Keyword:
Feature extraction
Convolutional neural networks
Prototypes
Hyperspectral imaging
Convolution
Data mining
Kernel
Attention mechanism
convolutional neural network (CNN)
graph convolutional network (GCN)
hyperspectral image (HSI) classification
prototype learning
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
.png){kind=icon}
Nearest neighbor classification of remote sensing images with the maximal margin principle基于最大间隔原理的遥感图像最近邻分类
PREAI
分享收藏
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
PREAI
分享收藏Hyperspectral and SAR Image Classification via Multiscale Interactive Fusion Network基于多尺度交互式融合网络的高光谱与SAR图像分类
PREAI
分享收藏
查看更多内容