Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data

This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a s...

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Bibliographic Details
Published in:Applied sciences 2019-11, Vol.9 (21), p.4620
Main Authors: Deng, Chubo, Zhang, Lifu, Cen, Yi
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
capsule network
Chemical oxygen demand
Datasets
Deep learning
Laboratories
Neural networks
Potassium
Remote sensing
Spectrum analysis
Statistical analysis
Teaching methods
Water pollution
Water quality
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Summary:This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and COD values. The results indicate there is a statistically significant correlation between COD and the hyperspectral data. The accuracy of the capsule network was compared with the results obtained from using a traditional back-propagation neural network (BP) method. The capsule network achieved superior accuracy with fewer iterations, compared with the BP algorithm. An R2 value of 0.78 was obtained against measured COD values retrieved using the capsule network method, compared with a value of 0.42 for the BP algorithm retrievals. This suggests the capsule network method has great potential to solve regression problems in the field of remote sensing.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9214620