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

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 t...

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Bibliographic Details
Main Authors: Xuemei Zhao, Jun Wu, Haijian Wang, Xingyu Gao, Longlong Zhao
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:International Journal of Digital Earth
Subjects:
landsat image classification
imbalanced and noisy label
convolutional neural network (cnn)
transferability
feature fusion
information entropy
Online Access:http://dx.doi.org/10.1080/17538947.2022.2036833

Internet

http://dx.doi.org/10.1080/17538947.2022.2036833

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