Injecting spectral indices to transferable convolutional neural network under imbalanced and noisy labels for Landsat image classification
Injecting spectral indices to transferable convolutional neural network under imbalanced and noisy labels for Landsat image classification
复制标题
在不平衡和噪声标签下将光谱索引注入可转移卷积神经网络以进行陆地卫星图像分类
DOI：
10.1080/17538947.2022.2036833
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发表时间：
2022-02
影响因子：
5.1
通讯作者：
Longlong Zhao
中科院分区：
文献类型：
--
作者：
Xuemei Zhao;Jun Wu;Haijian Wang;Xingyu Gao;Longlong Zhao
摘要稳定、连续的遥感土地覆盖制图对农业、生态系统和土地管理具有重要意义。卷积神经网络（CNN）是实现这一目标的有前途的方法。然而，训练CNN所需的大量高质量训练样本很难获得。在实践中，源自现有土地覆盖图的不平衡和嘈杂的标签可作为替代办法。实验表明，训练样本的不一致性对CNN的性能有很大影响。为了克服这个缺点，提出了一种方法，注入高度一致的信息到网络中，学习一般和可转移的功能，以减轻不完美的训练样本的影响。光谱指数是能够提供一致信息的重要特征。这些索引可以与CNN特征图融合，CNN特征图利用信息熵来选择最合适的CNN层，以补偿由不平衡的噪声标签引起的不一致性。建议的可转移CNN，测试与区域间Landsat时间序列的不平衡和噪声标签，不仅是上级的土地覆盖映射的精度，但也表现出良好的可转移性区域之间的时间序列和跨区域的Landsat图像分类。
ABSTRACT Stable and continuous remote sensing land-cover mapping is important for agriculture, ecosystems, and land management. Convolutional neural networks (CNNs) are promising methods for achieving this goal. However, the large number of high-quality training samples required to train a CNN is difficult to acquire. In practice, imbalanced and noisy labels originating from existing land-cover maps can be used as alternatives. Experiments have shown that the inconsistency in the training samples has a significant impact on the performance of the CNN. To overcome this drawback, a method is proposed to inject highly consistent information into the network, to learn general and transferable features to alleviate the impact of imperfect training samples. Spectral indices are important features that can provide consistent information. These indices can be fused with CNN feature maps which utilize information entropy to choose the most appropriate CNN layer, to compensate for the inconsistency caused by the imbalanced, noisy labels. The proposed transferable CNN, tested with imbalanced and noisy labels for inter-regional Landsat time-series, not only is superior in terms of accuracy for land-cover mapping but also demonstrates excellent transferability between regions in both time series and cross-regional Landsat image classification.
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DOI：
10.23919/fusion45008.2020.9190246
发表时间：
2020-07
期刊：
2020 IEEE 23rd International Conference on Information Fusion (FUSION)
影响因子：
--
作者：
K. Gadzicki;Razieh Khamsehashari;C. Zetzsche
通讯作者：
K. Gadzicki;Razieh Khamsehashari;C. Zetzsche
影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
--
作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
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作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
DOI：
10.4018/978-1-5225-7862-8.ch007
发表时间：
2019
期刊：
Handbook of Research on Deep Learning Innovations and Trends
影响因子：
--
作者：
K. Lakhtaria;Darshankumar Modi
通讯作者：
K. Lakhtaria;Darshankumar Modi