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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Updated: Aug 13, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Spatial response resampling (SR2): Accounting for the spatial point spread function in hyperspectral image

Deep Inamdar1, Margaret Kalacska1, Patrick Osei Darko1

  • 1Applied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada.

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|January 20, 2023
PubMed
Summary
This summary is machine-generated.

A new spatial response resampling (SR²) workflow simulates hyperspectral imaging (HSI) data degradation. This open-source tool accurately models sensor spatial response for realistic HSI data simulation and analysis.

Keywords:
Data cross-validationData fusionFlight planningMATLABPoint spread functionPushbroomSimulationSpatial Response Resampling (SR2)Spatial resamplingSpatial response

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Area of Science:

  • Remote Sensing
  • Geospatial Data Analysis
  • Image Processing

Background:

  • Hyperspectral imaging (HSI) data availability is increasing across various scales.
  • Existing methods for spatial degradation of HSI data lack realism, producing overly sharp images.
  • There is a need for tools that accurately simulate sensor spatial response for HSI data analysis.

Purpose of the Study:

  • To introduce the spatial response resampling (SR²) workflow for degrading georeferenced raster HSI data.
  • To provide an open-source and accessible tool for realistic HSI data simulation.
  • To demonstrate the utility of the SR² workflow in practical HSI applications.

Main Methods:

  • The SR² workflow derives the point spread function (PSF) of a target HSI sensor using acquisition parameters.
  • It convolves the derived PSF with a finer resolution HSI dataset to simulate coarser resolution imagery.
  • A MATLAB function is provided to implement the SR² methodology for end-user accessibility.

Main Results:

  • The SR² workflow generates more realistic degraded HSI data compared to traditional methods.
  • The workflow successfully simulates HSI data degradation based on specific sensor characteristics.
  • Practical applications demonstrated include data cross-validation, flight planning, and sensor data fusion.

Conclusions:

  • The SR² workflow offers a novel and accurate approach to spatial degradation of HSI data.
  • Its open-source nature and MATLAB implementation promote widespread adoption and use.
  • Accurate HSI data simulation using SR² is crucial for reliable data analysis and applications.