计算机科学
高光谱成像
像素
人工智能
图形
卷积神经网络
模式识别(心理学)
特征提取
卷积(计算机科学)
背景(考古学)
上下文图像分类
保险丝(电气)
计算机视觉
图像(数学)
人工神经网络
理论计算机科学
古生物学
电气工程
生物
工程类
作者
Wei Wang,Kun Gao,Xiang Zhang,Jing Wang,Zhanli Hu,Zhijia Yang,Yongyi Mao,Ying Liu
标识
DOI:10.1109/igarss52108.2023.10282995
摘要
Graph convolutional network (GCN) gains increasing attention in the hyperspectral image (HSI) classification by the ability to flexibly capture arbitrarily irregular objects. However, due to expensive computation, the graph construction is usually based on superpixel-wise nodes, which ignore the subtle pixel-wise features. In contrast, the convolution neural network (CNN) can mine pixel-wise spectral-spatial features but is limited to capturing local features in small square windows. In this paper, we design a new CNN and GCN collaborative network to simultaneously introduce pixel- and patch-wise contextual information. Concretely, we use the depthwise separable convolution to perform pixel-wise local feature extraction. To further mine the long-range contextual information between land covers, we concatenate a GCN. Finally, we further fuse the complementary features and decode them to obtain the classification map. Extensive experiments reveal that our method achieves competitive performance.
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