Image reconstruction by parametric cubic convolution

Park, Stephen K.; Schowengerdt, Robert A.

doi:10.1016/0734-189x(83)90026-9

Cited by 311 publications

(147 citation statements)

References 5 publications

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“…Terrain correction is performed using the USGS 1-arc second National Elevation Dataset (NED) (Gesch et al, 2002) to improve geolocation accuracy. The selection of cubic convolution as a resampling strategy was based largely on the superior spatial accuracy it provides over nearest neighbor resampling (Shilen 1979;Park and Schowengerdt, 1982). This is of special concern when stacking multiple dates across many path/rows, as is the case with NLCD 2001.…”

Section: Preprocessingmentioning

confidence: 99%

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

Ervin

Holly

2011

Invasive plant sci. manag.

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Multi-Resolution Land Characterization 2001 (MRLC 2001) is a second-generation Federal consortium designed to create an updated pool of nation-wide Landsat 5 and 7 imagery and derive a second-generation National Land Cover Database (NLCD 2001 IntroductionConsistent, relevant land cover information at a national scale provides data for a wide variety of geographical analysis and applications. In the last decade, a major provider of land cover information within the Federal government has been the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC was originally formed in 1993, to meet the needs of several Federal agencies (U.S. Geological Survey (USGS), Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and U.S. Forest Service (USFS)) for Landsat 5 imagery and land cover information (Loveland and Shaw 1996). One of the products of this consortium was the completion of a successful mapping of the conterminous United States into the National Land Cover Dataset (NLCD 1992) (NLCD 2001), which is being generated across all 50 States and Puerto Rico using Landsat imagery and ancillary data.The completion of the initial NLCD 1992 (Vogelmann et al., 2001A) created a TM pixel scale (30 m) data layer over the conterminous United States with approximately nine billion pixels. During the five years of mapping required to complete this prototype product, many lessons were learned about quality of source data, objectivity of methods, and flexibility of products. This feedback, coupled with new MRLC 2001 member requirements, provided the guiding principles and research direction that culminated in the NLCD 2001 design. Principles included: (a) develop land cover products flexible enough for multiple users, (b) provide users with increased access to intermediate database products and derivatives, enabling local application, (c) develop methods that are as objective, consistent, and repeatable as possible, resulting in standardized land cover products that can be quickly updated, (d) constrain methods to those that are intuitive, simple, efficient, and transferable to others, and (e) ensure that the design of a second-generation land cover product maintains reasonable compatibility with NLCD 1992.The NLCD 2001 foundation is a database approach to land cover (defined as multiple interlinked data layers that are useful either as individual components or in synergistic groupings) which builds upon past USGS database designs such as the global land cover database (Brown et al., 1999, Loveland et al., 2001, while providing the land cover data necessary to meet the vision of the The National Map (USGS 2001) currently being created by the USGS for the United States. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING July 2004829 SAIC Corporation, USGS/EROS Data Center, Sioux Falls, SD 57198 (homer@usgs.gov).

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Section: Preprocessingmentioning

confidence: 99%

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

Ervin

Holly

2011

Invasive plant sci. manag.

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“…Specifically (18) Proof: A basic application of Stirling's formula, , provides . Taking the logarithm of in (17), we readily obtain (18) after some manipulations which involve and .…”

Section: )mentioning

confidence: 99%

“…Others observed that what we will call "approximation order" improves quality [15]- [17]. An example is that of Keys' cubic kernel which has a free parameter; its optimization turns out to be the one that provides the highest approximation order [18]. In order to provide computationally efficient algorithms, these kernels were chosen to be piecewise-polynomial and of short support.…”

Section: Introductionmentioning

confidence: 99%

MOMS: maximal-order interpolation of minimal support

Blu

ThCvenaz²,

Unser

2001

IEEE Trans. on Image Process.

181

164

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Abstract-We consider the problem of interpolating a signal using a linear combination of shifted versions of a compactly-supported basis function ( ). We first give the expression of the 's that have minimal support for a given accuracy (also known as "approximation order"). This class of functions, which we call maximal-order-minimal-support functions (MOMS) is made of linear combinations of the B-spline of same order and of its derivatives.We provide the explicit form of the MOMS that maximize the approximation accuracy when the step-size is small enough. We compute the sampling gain obtained by using these optimal basis functions over the splines of same order. We show that it is already substantial for small orders and that it further increases with the approximation order . When is large, this sampling gain becomes linear; more specifically, its exact asymptotic expression is 2 ( ) . Since the optimal functions are continuous, but not differentiable, for even orders, and even only piecewise continuous for odd orders, our result implies that regularity has little to do with approximating performance.These theoretical findings are corroborated by experimental evidence that involves compounded rotations of images.

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“…We took particular care in the interpolation algorithm to avoid creating some spurious lensing signal or introducing additional non-Gaussianities. We found that a parametric cubic interpolation scheme (Park & Schowengerdt 1983) fitted reasonably well. In addition, to avoid any loss of power due to the interpolation, we overpixellized twice the underlying unlensed temperature and deflection field.…”

Section: Lensed Cmb Temperature Mapmentioning

confidence: 80%

Reconstruction of the cosmic microwave background lensing forPlanck

Perotto¹,

Bobin²,

Plaszczynski³

et al. 2010

A&A

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Aims. We prepare real-life cosmic microwave background (CMB) lensing extraction with the forthcoming Planck satellite data by studying two systematic effects related to the foreground contamination: the impact of foreground residuals after a component separation on the lensed CMB map, and the impact of removing a large contaminated region of the sky. Methods. We first use the generalized morphological component analysis (GMCA) method to perform a component separation within a simplified framework, which allows a high statistics Monte-Carlo study. For the second systematic, we apply a realistic mask on the temperature maps and then restore them with a recently developed inpainting technique on the sphere. We investigate the reconstruction of the CMB lensing from the resultant maps using a quadratic estimator in the flat sky limit and on the full sphere. Results. We find that the foreground residuals from the GMCA method does not significantly alter the lensed signal, which is also true for the mask corrected with the inpainting method, even in the presence of point source residuals.

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Image reconstruction by parametric cubic convolution

Cited by 311 publications

References 5 publications

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

MOMS: maximal-order interpolation of minimal support

Reconstruction of the cosmic microwave background lensing forPlanck

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