Study on chlorophyll a concentration inversion method based on principal component analysis and BP neural network
Aiming at the correlation between MODIS different bands and the impact of information redundancy between different bands on Chl concentration modeling,the principal component analysis(PCA)method was introducec into the modeling and a Chl concentration inversion model combining PCA and BP network(PCA-BPN)was proposed in this paper.Through principal component analysis,several independent key principal components were extracted from multiple related bands,and then these key principal components were used as the input of BP network to construct the Chl concentration inversion model through network autonomous learning.Experimental results showed that the first three principal components contained 99.5％of the band information,which reduced the input dimension of the neural network.Compared with the MODISA standard Chl inversion model OCI,PCA-BPN model had higher inversion accuracy and certain application potential in the retrieval of Chl concentration in the global ocean water.
MODIS satelliteprincipal component analysisartificial neural networkschlorophyll concentrationinversion model
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NSTL
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