高光谱成像
计算机科学
人工智能
深度学习
数据集
集合(抽象数据类型)
模式识别(心理学)
图像(数学)
计算
训练集
数据建模
机器学习
功能(生物学)
上下文图像分类
数据挖掘
算法
数据库
程序设计语言
进化生物学
生物
作者
Lei Zhao,Yi Zeng,Peng Liu,Xiaohui Su
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
被引量:10
标识
DOI:10.1109/lgrs.2020.3045437
摘要
In hyperspectral image classification, a large number of labeled samples are necessary for deep network training. However, labeling of hyperspectral images is tedious, difficult, and time-consuming work. In this letter, a new active learning (AL) framework for deep networks is proposed. An auxiliary deep network for the basic learner is constructed to learn the uncertainty of unlabeled samples in the candidate data set. Both the features of the original training data and the features of the middle hidden layer of the basic learner are gathered into a fully connected network with a newly defined loss function. In order to avoid the problem of an insufficient number of samples or a large amount of computation in AL, data sampling is performed on a candidate data set, and augmentation is conducted on newly selected samples. The proposed model is evaluated on different data sets and compared with other related methods. The results show that the proposed model performs better on different data sets than other methods.
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