On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction

IF 4 3区 地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-07-01 DOI:10.1109/LGRS.2020.3011549
C. Muehlmann, K. Nordhausen, Mengxi Yi
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引用次数: 9

Abstract

Multivariate measurements taken at irregularly sampled locations are a common form of data, for example, in geochemical analysis of soil. In practical considerations, predictions of these measurements at unobserved locations are of great interest. For standard multivariate spatial prediction methods it is mandatory to not only model spatial dependencies but also cross-dependencies which makes it a demanding task. Recently, a blind source separation (BSS) approach for spatial data was suggested. When using this spatial BSS (SBSS) method before the actual spatial prediction, modeling of spatial cross-dependencies is avoided, which in turn simplifies the spatial prediction task significantly. In this letter, we investigate the use of SBSS as a preprocessing tool for spatial prediction and compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical data set.
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多元空间预测的Cokriging、神经网络和空间盲源分离
在不规则采样的位置进行的多变量测量是一种常见的数据形式,例如,在土壤的地球化学分析中。在实际考虑中,对未观测到的位置的这些测量结果的预测非常令人感兴趣。对于标准的多变量空间预测方法,不仅必须对空间相关性进行建模,还必须对交叉相关性进行建模——这使得它成为一项要求很高的任务。最近,提出了一种用于空间数据的盲源分离(BSS)方法。当在实际的空间预测之前使用这种空间BSS(SBSS)方法时,避免了空间交叉依赖性的建模,这反过来大大简化了空间预测任务。在这封信中,我们研究了SBSS作为空间预测预处理工具的使用,并将其与广泛模拟研究中的Cokriging和神经网络的预测以及地球化学数据集进行了比较。
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来源期刊
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理
CiteScore
7.60
自引率
12.50%
发文量
1113
审稿时长
3.4 months
期刊介绍: IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.
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