Hyperspectral Imagery Classification Based on Contrastive Learning
Hyperspectral Imagery Classification Based on Contrastive Learning
复制标题
基于对比学习的高光谱图像分类
DOI：
10.1109/tgrs.2021.3139099
复制
发表时间：
2022
影响因子：
8.2
通讯作者：
Licheng Jiao
中科院分区：
文献类型：
--
作者：
Sikang Hou;Hongye Shi;Xianghai Cao;Xiaohua Zhang;Licheng Jiao
有监督机器学习和深度学习方法在高光谱图像分类中表现良好。然而，高光谱图像的标记样本很少，这使得监督分类方法对样本数量和质量的依赖程度很高，难以对其进行训练。受自监督学习思想的启发，提出了一种基于对比学习的高光谱图像分类算法，该算法利用大量未标注样本的信息来缓解高光谱数据中标注信息不足的问题。该算法采用两阶段训练策略。在第一阶段，模型采用自监督学习的方式进行预训练，利用大量未标记样本结合数据增强构造正负样本对，并进行对比学习。其目的是使模型能够对正样本和负样本做出判断。在第二阶段，基于预先训练的模型，提取高光谱图像的特征进行分类，并使用少量的标记样本对特征进行微调。实验表明，自监督学习提取的特征在后续分类任务中取得了较好的效果。
Supervised machine learning and deep learning methods perform well in hyperspectral image classification. However, hyperspectral images have few labeled samples, which make them difficult to be trained because supervised classification methods rely heavily on sample quantity and quality. Inspired by the idea of self-supervised learning, this article proposes a hyperspectral imagery classification algorithm based on contrast learning, which uses the information of abundant unlabeled samples to alleviate the problem of insufficient label information in hyperspectral data. The algorithm uses a two-stage training strategy. In the first stage, the model is pretrained in the way of self-supervised learning, using a large number of unlabeled samples combined with data enhancement to construct positive and negative sample pairs, and contrastive learning (CL) is carried out. The purpose is to enable the model to make judgments on positive and negative samples. In the second stage, based on the pretrained model, the features of the hyperspectral image are extracted for classification, and a small amount of labeled samples are used to fine-tune the features. Experiments show that the features extracted by self-supervised learning achieved improved results on downstream classification task.