Principal component analysis applied to remote sensing

Estornell, Javier; Martí-Gavliá, Jesus M.; Sebastià, M. T.; Cuquerella, Jesús Mengual

doi:10.4995/msel.2013.1905

Cited by 68 publications

(39 citation statements)

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“…[53][54][55][56][57][58] It is particularly effective in the analysis of changes in multitemporal image datasets. The purpose of using PCA is to reduce the dimensionality of the data, i.e., the number of original bands, to characterize the amount of information into a few principal components.…”

Section: Principal Component Analysis Utilitymentioning

confidence: 99%

“…The purpose of using PCA is to reduce the dimensionality of the data, i.e., the number of original bands, to characterize the amount of information into a few principal components. 57 58 Even the tasseled cap (TC) transformation is based on the method of the PCA combined with empirical observations. [53][54][55][56][57][58] It is particularly effective in the analysis of changes in multitemporal image datasets.…”

Section: Principal Component Analysis Utilitymentioning

confidence: 99%

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Spectral index utility for summer urban heating analysis

Bonafoni

2015

J. Appl. Remote Sens

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The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index.Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.

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Section: Principal Component Analysis Utilitymentioning

confidence: 99%

Section: Principal Component Analysis Utilitymentioning

confidence: 99%

Spectral index utility for summer urban heating analysis

Bonafoni

2015

J. Appl. Remote Sens

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“…Most successful attempts have the PC eigenvalue standard from 1.5 to 5.4 (Estornell et al 2013;Munyati 2004;Jensen 1996). This condition was chosen based on the common practice in PCA applications, since there is less specific information regarding the standard when dealing with remotely sensed images.…”

Section: Methodsmentioning

confidence: 99%

Temporal Downscaling of TRMM Rain-Rate Images Using Principal Component Analysis during Heavy Tropical Thunderstorm Seasons

Mahmud

Matsuyama

Hosaka

et al. 2015

Journal of Hydrometeorology

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This paper examines the utility of principal component analysis (PCA) in obtaining accurate daily rainfall estimates from 3-hourly Tropical Rainfall Measuring Mission (TRMM) satellite data during heavy precipitation in a humid tropical environment. A large bias during heavy thunderstorms in humid tropical catchments is indicated by the TRMM satellite and is of profound concern because it is a conspicuous constraint for practical hydrology applications and requires proper treatment, particularly in areas with sparse rain gauges. The common procedure of calculating daily rainfall estimates by direct accumulation (DA) of a series of 3-hourly rainfall estimates caused a large bias because of temporal uncertainties, upscaling effects, and different mechanisms. In this study, PCA was used to transform correlated 3-hourly rain-rate images into a minimum effective principal component and to compute the corresponding rain-rate proportion based on correlation strength. This study was conducted on 91 rainy days of various intensity, acquired from three different years, during the wettest season on the eastern coast of peninsular Malaysia. Results showed that PCA reduced the bias and daily root-mean-square error by an average of 62% and 22%, respectively, compared with the DA approach. The PCA transformation was able to produce more precise daily rainfall estimates compared to the DA approach without the use of any rain gauge references. However, the performance was varied by the threshold selection and rainfall intensity. The results of this study indicate that PCA can be a useful tool in effective temporal downscaling of TRMM satellite data during heavy thunderstorm seasons in areas where rain gauges are sparse and satellite data are pivotal as a secondary source of rainfall data.

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“…The first (schistose sandstone) is Precambrian A-Tabular of higher formations, and the second (sandstone of granules) is Precambrian A-Tabular of medium formations. Principal Component Analysis (PCA) consists of defining new channels that summarize the information contained in an image in multispectral space [19,20]. This method aims to maximize (statistically) the amount of information (or variance) of original data in a restricted number of components [11,21].…”

Section: Lithological Mappingmentioning

confidence: 99%

Spatial Analysis of Linear Structures in the Exploration of Groundwater

Dembele

2017

IJGI

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Abstract:The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding), Principal Component Analysis (PCA), and band ratio methods. The automatic extraction method of linear structures has permitted the obtaining of a structural map with 82,659 linear structures, distributed on different stratigraphic stages. The intensity study shows an accentuation in density over 12.52% of the total area, containing 22.02% of the linear structures. The density and nodes (intersections of fractures) formed by the linear structures on the different lithologies allowed to observe the behavior of the region's aquifers in the exploration of subsoil resources. The central density, in relation to the hydrographic network of the lowlands, shows the conditioning of the flow and retention of groundwater in the region, and in-depth fluids. The node areas and high-density linear structures, have shown an ability to have rejections in deep (pores) that favor the formation of structural traps for oil resources.

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Principal component analysis applied to remote sensing

Cited by 68 publications

References 1 publication

Spectral index utility for summer urban heating analysis

Spectral index utility for summer urban heating analysis

Temporal Downscaling of TRMM Rain-Rate Images Using Principal Component Analysis during Heavy Tropical Thunderstorm Seasons

Spatial Analysis of Linear Structures in the Exploration of Groundwater

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