Evidence map›Paper›PMID 36660342›Full record

ArticleMethodsX2023

Spatial response resampling (SR

Deep Inamdar, Margaret Kalacska, Patrick Osei Darko, J Pablo Arroyo-Mora, George Leblanc

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Deep InamdarApplied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada.
Margaret KalacskaApplied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada.
Patrick Osei DarkoApplied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada.
J Pablo Arroyo-MoraFlight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada.
George LeblancFlight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increased availability of hyperspectral imaging (HSI) data at various scales (0.03-30 m), the role of simulation is becoming increasingly important in data analysis and applications. There are few commercially available tools to spatially degrade imagery based on the spatial response of a coarser resolution sensor. Instead, HSI data are typically spatially degraded using nearest neighbor, pixel aggregate or cubic convolution approaches. Without accounting for the spatial response of the simulated sensor, these approaches yield unrealistically sharp images. This article describes the spatial response resampling (SR

Indexed as

Data cross-validationData fusionFlight planningMATLABPoint spread functionPushbroomSimulationSpatial resamplingSpatial responseSpatial Response Resampling (SR2)

Identifiers

PMID36660342
PMCPMC9842868

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.