基于奇异谱分析的改进遥感时空融合模型

doi:10.6046/zrzyyg.2022095

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摘要

时空融合能够生成具有足够时间和空间分辨率的图像序列。然而,当前的研究趋向于使用尽可能多的时相数据、复杂的非线性模型来提高预测的准确性,极少的研究将重点放在图像本身的分析,即充分利用影像包含的如趋势和纹理等内在特征。为此,文章基于二维奇异谱分析(2D singular spectrum analysis,2DSSA)技术,提出了一种2DSSA时空融合模型(2DSSA spatial-temporal fusion model,2DSSA-STFM),通过将已有影像分解为趋势分量和细节分量,分别对目标时刻影像的主要空间趋势和空间细节进行预测。首先,建立高空间分辨率数据趋势项与低空间分辨率数据的线性关系,计算得到目标时刻影像的趋势成分; 然后,建立2个时相下低分辨率细节分量和高分辨率细节分量的线性关系,得到目标时刻影像的细节成分; 最后,将计算得到的趋势和细节成分进行合成,即为目标预测影像。在2组中分辨率Landsat7 ETM+和MODIS影像上对提出的2DSSA-STFM进行了实验,结果表明,提出的模型在实验误差方面要优于传统的时空融合模型。

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	安娜
	赵莹莹
	孙娅琴
	张爱竹
	付航
	姚延娟
	孙根云

关键词 ：时空融合, 奇异谱分析, 趋势分量, 细节分量, 分解

Abstract：

Spatio-temporal fusion can generate image sequences with sufficiently high temporal and spatial resolution. However, current studies tend to improve prediction accuracy using as much spatio-temporal data as possible and complex non-linear models, while few of them focus on analyzing images themselves by making full use of their intrinsic features, such as trends and textures. This study proposed a 2DSSA spatio-temporal fusion model (2DSSA-STFM) based on 2D singular spectrum analysis (2DSSA). In this model, the major spatial trends and details of the existing images at the target moment can be predicted by decomposing the images into trend and detail components. Firstly, the linear relationship between the trend of high-spatial-resolution data and low-spatial-resolution data was built to calculate the trend components of the images at the target moment. Then, the linear relationship between the low-resolution and the high-resolution detail components in two time phases was established to determine the detail components of the images at the target moment. Finally, the calculated trend and detail components were combined to form the target prediction images. The 2DSSA-STFM was applied to two sets of medium-resolution Landsat7 ETM+ and MODIS images, yielding smaller experimental errors than conventional spatio-temporal fusion models.

Key words： spatio-temporal fusion singular spectrum analysis trend component detail component decomposition

收稿日期: 2022-03-21 出版日期: 2023-07-07

ZTFLH:

TP753

基金资助:国家自然科学基金项目“饮用水源地保护区环境风险源变化多尺度遥感探测机制与不确定性研究”(41871270);“复杂城市地表不透水面多源高分遥感成像机理与分层优化提取方法”(41971292)

通讯作者: 姚延娟(1974-),女,博士,正高级工程师,主要从事多源遥感数据智能解译的研究。Email: yaoyj@secmep.cn。

作者简介: 安娜(1980-),女,硕士,高级工程师,主要从事遥感数据处理技术方法研究与矿产资源应用。Email: an_na826@163.com。

引用本文:

安娜, 赵莹莹, 孙娅琴, 张爱竹, 付航, 姚延娟, 孙根云. 基于奇异谱分析的改进遥感时空融合模型[J]. 自然资源遥感, 2023, 35(2): 89-96.
AN Na, ZHAO Yingying, SUN Yaqin, ZHANG Aizhu, FU Hang, YAO Yanjuan, SUN Genyun. An improved spatio-temporal fusion model for remote sensing images based on singular spectrum analysis. Remote Sensing for Natural Resources, 2023, 35(2): 89-96.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022095 或 https://www.gtzyyg.com/CN/Y2023/V35/I2/89

Fig.1 算法流程

Fig.2 数据集1的MODIS和Landsat7 ETM+影像

Fig.3 数据集2的MODIS和Landsat7 ETM+影像

Fig.4 数据集1预测的趋势项和细节项

Fig.5 数据集1预测图像

Tab.1 数据集1的定量评估结果

Fig.6 数据集2预测图像

Tab.2 数据集2的定量评估结果

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