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Spatial Coverage Map and Resampling Error Assessment for Hyperspectral Imaging Data
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Inamdar, D; Soffer, Raymond; Kalacska, Margaret; Naprstek, T — 2023-01-06 A set of MATLAB functions (HSI_PSFS, SC_RS_Analysis_NAD.m, SC_RS_Analysis_sim.m) were developed to assess the spatial coverage of pushbroom hyperspectral imaging (HSI) data. HSI_PSFs derives the net point spread function of HSI data based on nominal data acquisition and sensor parameters (sensor speed, sensor heading, sensor altitude, number of cross track pixels, sensor field of view, integration time, frame time and pixel summing level). SC_RS_Analysis_sim calculates a theoretical spatial coverage map for HSI data based on nominal data acquisition and sensor parameters. The spatial coverage map is the sum of the point spread functions of all the pixels collected within an HSI dataset. Practically, the spatial coverage map quantifies how HSI data spatially samples spectral information across an imaged scene. A secondary theoretical spatial coverage map is also calculated for spatially resampled (nearest neighbour approach) HSI data. The function also calculates theoretical resampling errors such as pixel duplication (%), pixel loss (%) and pixel shifting (m). SC_RS_Analysis_NAD calculates an empirical spatial coverage map for collected HSI data (before and after spatial resampling) based on its nominal data acquisition and sensor parameters. The function also calculates empirical resampling errors. The current implementation of SC_RS_Analysis_NAD only works for ITRES (Calgary, Alberta, Canada) data products as it uses auxiliary information generated during the ITRES data processing workflow. This auxiliary information includes a ground look-up table that specifies the location (easting and northing) of each pixel of the HSI data in its raw sensor geometry. This auxiliary information also includes the pixel-to-pixel mapping between the HSI data in its raw sensor geometry and the spatially resampled HSI data. SC_RS_Analysis_NAD can readily be modified to work with HSI data collected by sensors from other manufacturers so long as the required auxiliary information can be extracted during data processing.
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SRIX4VEG: Surface Reflectance Intercomparison Exercise for Vegetation - NRC, ARSL, NEO
Borealis
Arroyo-Mora, J. Pablo; Kalacska, Margaret; Soffer, Raymond; Lucanus, Oliver; Løke, Trond; Hernandez, Julio; Adamek, Dennis — 2023-11-10 Over the last ten years, the Applied Remote Sensing Laboratory (ARSL, McGill University) in collaboration with the National Research Council of Canada's Flight Research Laboratory (FRL), has been implementing cutting-edge unmanned aerial vehicle (UAV) hyperspectral systems for diverse applications (e.g., biodiversity, subaquatic vegetation, wildfires), with a significant focus on satellite product validation methodologies for northern ecosystems – e.g. peatlands. SRIX4Veg is endorsed by CEOS (Committee on Earth Observation Satellites) and is funded by the European Space Agency (ESA). The ARSL, FRL and Norsk Elektro Optikk HySpex teamed up to participate in the SRIX4VEG: Surface Reflectance Intercomparison Exercise for Vegetation experiment, at the Las Tiesas Experimental Station, Barrax, Spain. This intercomparison experiment included teams from Europe, US and Canada, aiming to develop a protocol for best practices for UAV hyperspectral image acquisition for satellite land product validation within ESA’s Fiducial Reference Measurements for Vegetation (FRM4VEG) project. Here we summarize the imagery acquired by our team during the field campaign at the July 18-22, 2022 field campaign. Meta data includes a description of the imagery acquired under the user-defined methodology. A future update will include metadata for the imagery acquired under the common experiment parameters defined by the organizers.
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Mer Bleue QA4EO Airborne Hyperspectral Imagery
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Soffer, Raymond; Arroyo-Mora, J. Pablo; Kalacska, Margaret; Ifimov, Gabriela; Leblanc, George — 2022-07-07 The data available consist of airborne hyperspectral imagery acquired for the <i>Mer Bleue Arctic Surrogate Simulation Site (MBASSS) S2/L8 Data Product Validation Project</i> in 2016. MBASSS was a collaborative effort aimed at developing a systematic approach for ongoing assessment and validation of satellite based land information products from Landsat 8 OLI and Sentinel 2 satellites. The airborne systems used for this project were the CASI-1500 and SASI-644 hyperspectral instruments (ITRES Research, Calgary AB) installed in the National Research Council Canada Flight Research Lab (NRC-FRL) Twin Otter aircraft. Standard level 2 processed imagery is provided for download as rasters in ENVI Standard format. Imagery is available from April 20, May 11, May 24 and June 23, 2016 as a set of 12 individual flight lines per date. The imagery has been atmospherically corrected and during the geocorrection process, it has been resampled to 1 m pixel size. Currently CASI and SASI imagery are provided separately. Metadata for each flight line is provided in external ascii ENVI header files (*.hdr) and *.met files. The geocorrected imagery provided with pixel level information including pixel view zenith angle (off Nadir angle), DEM, view azimuth angle, radiance path distance, column and row numbers of pixels in non-geocorrected image file, and relative pixel offset between calculated and assigned pixel location. This information is provided in associated *.nad and *.nad.hdr files.

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(Author: Soffer, Raymond)

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