| 一种基于深度传递迁移学习的遥感影像分类方法 |
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| 引用本文： | 林禹,赵泉华,李玉.一种基于深度传递迁移学习的遥感影像分类方法[J].地球信息科学,2022,24(3):495-507. |
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| 作者姓名： | 林禹 赵泉华 李玉 |
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| 作者单位： | 辽宁工程技术大学测绘与地理科学学院,阜新 123000 |
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| 基金项目： | 国家自然科学基金项目(41801233);国家自然科学基金项目(41801368) |
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| 摘 要： | 面对实际的遥感影像分类任务,采用深度神经网络的方法存在的最大问题是缺乏充足的标注样本,如何使用较少的标注样本实现较高精度的遥感影像分类,是目前需要解决的问题。ImageNet作为世界上最大的图像识别数据集,在其上训练出的模型有着丰富的底层特征。对ImageNet预训练模型进行微调是最常见的迁移学习方法,能够一定程度利用其丰富的底层特征,提高分类精度。但ImageNet影像特征与遥感影像差距较大,对分类效果提升有限。为了解决上述问题,本文基于传递迁移学习思想,结合深度神经网络,提出一种基于深度传递迁移学习的遥感影像分类方法。该方法通过构建以开源遥感场景识别数据集为源域的中间域,并以ImageNet预训练权重为源域、待分类遥感影像为目标域进行迁移学习,提高遥感影像分类精度。首先,以ImageNet预训练VGG16网络为基础,为加速卷积层权重更新而将全连接层替换为全局平均池化层,构建GAP-VGG16,使用中间域数据集训练ImageNet预训练GAP-VGG16以获取权重;然后,以SegNet网络为基础,在SegNet中加入卷积层设计了T-SegNet,以对获取的权重进一步地提取。最后,将获取的权重迁移到T-SegNet中,使用目标域数据集训练,实现遥感影像分类。本文选取Aerial Image Dataset和UC Merced Land-Use DataSet作为中间域数据集的数据源,资源三号盘锦地区影像为目标域影像,并分别选取了50%和25%数量的训练样本进行实验。实验结果表明,在50%和25%数量的训练样本下,本文方法分类结果相比SegNet的Kappa系数分别提高了0.0459和0.0545,相比ImageNet预训练SegNet的Kappa系数分别提高了0.0377和0.0346,且在样本数较少的类别上,本文方法分类精度提升更明显。
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| 关 键 词： | 遥感影像分类 深度神经网络 迁移学习 VGG16 SegNet 传递迁移学习 资源三号影像 全局平均池化 |
| 收稿时间： | 2021-07-25 |
A Remote Sensing Image Classification Method based on Deep Transitive Transfer Learning |
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| LIN Yu,ZHAO Quanhua,LI Yu.A Remote Sensing Image Classification Method based on Deep Transitive Transfer Learning[J].Geo-information Science,2022,24(3):495-507. |
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| Authors: | LIN Yu ZHAO Quanhua LI Yu |
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| Institution: | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
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| Abstract: | In the practical task of remote sensing image classification, the biggest problem with the use of deep neural network method is the lack of sufficient labeled samples. How to use fewer labeled samples to achieve higher accuracy of remote sensing image classification is a problem that needs to be solved at present. ImageNet is the largest image recognition dataset in the world, the model trained on it has rich underlying features. Fine-tuning the ImageNet pre-training model is the most common transfer learning method, which can make use of the rich underlying features to improve the classification accuracy. However, there is a big difference between ImageNet image features and remote sensing image features, and the improvement of classification effect is limited. In order to solve the above problems, a remote sensing image classification method based on deep transitive transfer learning combined with deep neural network is proposed in this paper. This method constructs an intermediate domain using the open-source remote sensing scene recognition datasets as the data source and uses ImageNet pre-training weight as the source domain and remote sensing images to be classified as the target domain for transfer learning to improve remote sensing image classification accuracy. First, based on ImageNet pre-training VGG16 network, the fully connected layer is replaced by the global average pooling layer in order to speed up the weight update of convolutional layer, and the GAP-VGG16 is constructed. The intermediate domain dataset is used for training the ImageNet pre-training GAP-VGG16 to obtain the weight. Then, based on the SegNet, the T-SegNet is designed by adding the convolutional layer into the SegNet to further extract the obtained weight. Finally, the obtained weight is transferred to T-SegNet, and the remote sensing image classification is achieved by training the target domain dataset. In this paper, the Aerial Image Dataset and UC Merced Land-Use Dataset are selected as the data sources of the intermediate domain dataset, and the ZY-3 Panjin area image is selected as the target domain image, 50% and 25% of the training samples are selected for the experiment. The experimental results show that using 50% and 25% of the training samples, the Kappa coefficient of the classification results using the proposed method in this paper is increased by 0.0459 and 0.0545, respectively compared to SegNet, and is increased by 0.0377 and 0.0346, respectively compared to ImageNet pre-training SegNet. For classes with a smaller number of samples, the classification accuracy of the method in this paper is improved more significantly. |
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| Keywords: | remote sensing classification deep neural network transfer learning VGG16 SegNet transitive transfer learning ZY-3 remote sensing image global average pooling |
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