Spatiotemporal fusion of remote sensing images based on particle swarm optimization and pixel decomposition

doi:10.6046/gtzyyg.2020.04.05

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Abstract

Spatiotemporal fusion image can meet the needs of large-scale, high-precision and rapid change of surface cover monitoring, and hence has been widely used in environmental, hydrological and agricultural monitoring and other fields. In this paper, based on different types of remote sensing data, the authors propose a method of remote sensing image fusion based on particle swarm optimization (PSO) and linear mixed pixel decomposition. First of all, the PSO method is applied to the calculation of the end-point reflectance through the statistics of the different range of the end-point reflectance, and then the remote sensing image fusion is realized by considering the difference of the end-point reflectance between the high and low spatial resolution images and the space-time weight. Finally, the comparison with the existing methods shows that the proposed method can effectively improve the accuracy of the prediction image produced by data fusion. The root mean square error and spatial structure similarity index predicted in this paper are better than the results of the enhanced spatial and temporal data fusion model(ESTDFM). Therefore, the proposed method would be of great value for the study of land cover change monitoring.

Keywords spatiotemporal fusion mixed pixel decomposition particle swarm optimization moving window

:	TP751
	P237

Corresponding Authors: LUO Weiran E-mail: 2383499173@qq.com;171230855@qq.com

Issue Date: 23 December 2020

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	Hongli ZHANG
	Weiran LUO
	Yan LI

Cite this article:

Hongli ZHANG,Weiran LUO,Yan LI. Spatiotemporal fusion of remote sensing images based on particle swarm optimization and pixel decomposition[J]. Remote Sensing for Land & Resources, 2020, 32(4): 33-40.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.05 OR https://www.gtzyyg.com/EN/Y2020/V32/I4/33

Fig.1 Technical flow chart

Fig.2 Study area of standard false color in near infrared, red and green bands

Fig.3 Change of $ERGA S h$ and $ERGA S l$

Fig.4 Results of standard false fusion in near infrared, red and green bands

Fig.5 Scatter diagram of predicted and observed values in near infrared, red and green bands

Tab.1 Accuracy evaluation results of two methods

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