Spatial Coverage Map and Resampling Error Assessment for Hyperspectral Imaging Data

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.