Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery

Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth scie...

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Bibliographic Details
Main Authors: Md Abul Ehsan Bhuiyan, Chandi Witharana, Anna K. Liljedahl, Benjamin M. Jones, Ronald Daanen, Howard E. Epstein, Kelcy Kent, Claire G. Griffin, Amber Agnew
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Journal of Imaging
Subjects:
deep learning
tundra
ice-wedge polygons
Mask R-CNN
satellite imagery
permafrost
Online Access:https://www.mdpi.com/2313-433X/6/9/97

Internet

https://www.mdpi.com/2313-433X/6/9/97

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