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Remote Sensing Technology and Application  2018, Vol. 33 Issue (5): 900-907    DOI: 10.11873/j.issn.1004-0323.2018.5.0900
Retrieval of Total Suspended Matter in the Lower of Minjiang River based on PSO-RBF
Xie Xu,Chen Yunzhi
(National Engineering Research Center of Geospatial Information Technology Key Laboratory of Spatial Data Mining & Information Sharing,Ministry of Education,Fuzhou 350116,China)
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Abstract  Total suspended matter (TSM) is one of the important parameters of water environment.As the spectral characteristics of the Case-II water are complicated,so it is not suitable to represent the relationship between spectral characteristics andTSM by simple linear models.In this paper,the test data,which is acquired by water quality sampling and spectral measurement of 40 points from July 12 -13,2017,together with GF-1 WFV1 bands reflectance data are used to analysis the correlation between remote sensing factors and TSM.Taking advantage of high correlation coefficients between bands,such as b3,b3/b2 and b3/b1,we construct PSO-RBF and RBF neural network model to inverse TSM.At the same time,a empirical b3/b2 ratio model is also proposed.The result shows that PSO-RBF neural network model’s performance is better than traditional RBF neural network and the empirical model,whose R2=0.890,RMSE=3.01 mg/L.On this basis,the GF-1 WFV1 remote sensing image is used to inverse TSM of Minjiang River,which is calculated by the well\|trained PSO-RBF model.Furthermore,the spatial distribution characteristics of TSM is also studied.The result of TSM inversion comes to RMSE=3.65 mg·L-1,MRE=14.11% respectively,and remote sensing image retrieval results accuracy was significantly higher than that of Kriging interpolation results,and there is
Key words:  Total suspended matter      Remote sensing reflectance      Mingjiang river      Particle Swarm Optimization      Neural network     
Received:  18 December 2017     
TP 79  
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Xie Xu
Chen Yunzhi

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Xie Xu,Chen Yunzhi. Retrieval of Total Suspended Matter in the Lower of Minjiang River based on PSO-RBF. Remote Sensing Technology and Application, 2018, 33(5): 900-907.

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