基于粒子群优化和像元分解模型的遥感影像时空融合

doi:10.6046/gtzyyg.2020.04.05

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摘要

时空融合影像能够满足大范围、高精度、快速变化的地表覆被监测需求,已被广泛应用于环境、水文及农情监测等多个领域。基于不同类型的遥感数据,提出一种基于粒子群优化(particle swarm optimization,PSO)和线性混合像元分解技术的遥感影像融合方法。首先,通过统计不同端元反射率变化范围将PSO方法应用于端元反射率求解过程中; 然后,综合考虑高低空间分辨率影像之间的端元反射率差异及时空权重实现遥感影像融合; 最后,与现有方法进行对比验证。结果表明,所提方法能够有效提高遥感影像的融合精度。以近红外、红光和绿光波段遥感影像为例,使用该方法得到的预测影像的均方根误差和空间结构相似指数均优于增强型的时空融合模型(enhanced spatial and temporal data fusion model,ESTDFM)方法,因此,该方法对地表覆被变化监测与研究具有实用意义。

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	张红利
	罗蔚然
	李艳

关键词 ：时空融合, 混合像元分解, 粒子群优化, 滑动窗口

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.

Key words： spatiotemporal fusion mixed pixel decomposition particle swarm optimization moving window

收稿日期: 2020-02-17 出版日期: 2020-12-23

:	TP751
	P237

基金资助:河南省高等学校青年骨干教师培养计划项目“基于Sentinel遥感数据的麦田土壤墒情反演”(2019GZGG073)

通讯作者: 罗蔚然

作者简介: 张红利(1982-),女,讲师,主要研究方向为工程测量研究。Email:2383499173@qq.com。

引用本文:

张红利, 罗蔚然, 李艳. 基于粒子群优化和像元分解模型的遥感影像时空融合[J]. 国土资源遥感, 2020, 32(4): 33-40.
ZHANG Hongli, LUO Weiran, LI Yan. Spatiotemporal fusion of remote sensing images based on particle swarm optimization and pixel decomposition. Remote Sensing for Land & Resources, 2020, 32(4): 33-40.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.04.05 或 https://www.gtzyyg.com/CN/Y2020/V32/I4/33

Fig.1 技术流程

Fig.2 研究区近红外、红光、绿光波段标准假彩色影像

Fig.3 $ERGA S h$ 和 $ERGA S l$ 变化

Fig.4 近红外、红光、绿光合成标准假彩色融合结果

Fig.5 近红外、红光、绿光波段预测值与观测值的二维散点图

Tab.1 2种方法的精度评估

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