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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9214620

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.