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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (2) : 34-40     DOI: 10.6046/gtzyyg.2016.02.06
Technology and Methodology |
Retrieving of salt lake mineral ions salinity from hyper-spectral data based on BP neural network
ZHOU Yamin1, ZHANG Rongqun1, MA Hongyuan1, ZHANG Jian2, ZHANG Xiaoshuan1
1. College of Information & Electrical Engineering, China Agriculture University, Beijing 100083, China;
2. College of Economic and Management, Beijing Information Science & Technology University, Beijing 100192, China
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Abstract  

Hyper-spectral remote sensing data can provide more spectral information and describe the spectral signature of salt lake more accurately than multi-spectral remote sensing data. Based on the theory of remote sensing on water, the authors analyzed the band correlation and information of HJ-1A satellite hyper-spectrum image by using adaptive band selection(ABS) method. Combined with BP neural network techniques, the authors determined the optimal band combination, established the retrieval models for mineral ions salinity of salt lake, quantitatively determined the salinities of K+, Mg2+, Na+, Cl-, SO42- ions of west Taijinar Salt Lake in Qaidam Basin, and acquired the spatial distribution siuation of mineral ions salinity. The results show that the forecast accuracy of BP neural network models are exclusively higher than 85%, the spatial distribution of mineral ions content of salt lake is consistent with the result of field survey. The research confirms that the correlation of BP neural network and domestic hyper-spectral remote sensing data can be used to monitor the mineral resource of salt lake dynamically, thus providing the scientific foundation for the reasonable development and efficient utilization.

Keywords rule set      spatio-temporal topological relationships of strata      GIS      checking system     
:  TP751.1  
Issue Date: 14 April 2016
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XIONG Yihui
WANG Xinqing
LI Guoqing
Cite this article:   
XIONG Yihui,WANG Xinqing,LI Guoqing. Retrieving of salt lake mineral ions salinity from hyper-spectral data based on BP neural network[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 34-40.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.06     OR     https://www.gtzyyg.com/EN/Y2016/V28/I2/34

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