计算机科学
人工智能
模式识别(心理学)
联营
高光谱成像
特征(语言学)
一般化
深度学习
相似性(几何)
特征提取
嵌入
关系(数据库)
特征学习
图像(数学)
训练集
数据挖掘
数学
数学分析
哲学
语言学
标识
DOI:10.1016/j.engappai.2023.106993
摘要
Deep learning methods have significantly progressed in hyperspectral image (HSI) classification. However, deep learning relies on large labeled data for training. The cost of labeling samples is enormous. In practical classification tasks, only a few labeled samples are available. A lightweight dense relation network with attention (LDA-RN) was presented for HSI few-shot classification. First, we design a 3D feature embedding module for spectral–spatial feature extraction. A light attention module (CSA), including the channel attention (CA) submodule and spatial attention (SA) submodule, is applied to reinforce essential features. Dense connections are used to fully utilize the feature information in each layer. Second, we propose a 2D relation learning module for classification by comparing the similarity between samples, using global average pooling to reduce the model parameters. Finally, meta-learning and fine-tuning training techniques are employed to enhance the model’s generalization ability and reduce the dependence on the labeled samples. Experimental results on three available HSI datasets suggest that the proposed LDA-RN outperforms existing advanced few-shot learning methods. For example, the experimental results show that the overall accuracy of the proposed method improves by 5.67% on the Univerisity of Pavia data set with only five labeled samples compared to the best method DCFSL.
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