The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data

Abstract

The raster data model has been the standard format for hyperspectral imaging (HSI) over the last four decades. Unfortunately, it misrepresents HSI data because pixels are not natively square nor uniformly distributed across imaged scenes. To generate end products as rasters with square pixels while preserving spectral data integrity, the nearest neighbor resampling methodology is typically applied. This process compromises spatial data integrity as the pixels from the original HSI data are shifted, duplicated and eliminated so that HSI data can conform to the raster data model structure. Our study presents a novel hyperspectral point cloud data representation that preserves the spatial-spectral integrity of HSI data more effectively than conventional square pixel rasters. This Directly-Georeferenced Hyperspectral Point Cloud (DHPC) is generated through a data fusion workflow that can be readily implemented into existing processing workflows used by HSI data providers. The effectiveness of the DHPC over conventional square pixel rasters is shown with four HSI datasets. These datasets were collected at three different sites with two different sensors that captured the spectral information from each site at various spatial resolutions (ranging from ∼1.5 cm to 2.6 m). The DHPC was assessed based on three data quality metrics (i.e., pixel loss, pixel duplication and pixel shifting), data storage requirements and various HSI applications. All of the studied raster data products were characterized by either substantial pixel loss (∼50–75%) or pixel duplication (∼35–75%), depending on the resolution of the resampling grid used in the nearest neighbor methodology. Pixel shifting in the raster end products ranged from 0.33 to 1.95 pixels. The DHPC was characterized by zero pixel loss, pixel duplication and pixel shifting. Despite containing additional surface elevation data, the DHPC was up to 13 times smaller in file size than the corresponding rasters. Furthermore, the DHPC consistently outperformed the rasters in all of the tested applications which included classification, spectra geo-location and target detection. Based on the findings from this work, the developed DHPC data representation has the potential to push the limits of HSI data distribution, analysis and application.