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.

Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization

“…Despite being sparser than the SfM, the UAS LiDAR data are considerably higher in density than conventional airborne LiDAR data from manned aircraft due to the low altitude of the UAS data collection. For example, airborne LiDAR data over the same study area produced a point cloud with a density of 2-4 pts/m 2 [59]. Similarly, the authors in [64] reported a point density of 1-2 pts/m 2 from airborne LiDAR for wetlands in Eastern Canada.…”

“…To classify the hummocks and hollows, the DSMs were first normalized in MATLAB v2020b (MathWorks, Natick, MA, USA) by subtracting the median elevation in a sliding window of 10 × 10 m [59]. Hummocks were defined as having a height range 5-31 cm above the median and hollows as >5 cm below the median.…”

“…As input to models that require a DSM as part of the workflow or as a covariate, e.g., peat burn intensity mapping [68], biomass estimation [59], and peat depth estimation [21], either the SfM or LiDAR would be sufficient. Both retain the gross microtopography of the bog, with similar semivariogram ranges and complexity (at the 50 cm scale).…”

Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

“…Due to their reliance on raster datasets, this correction method performs poorly in topographically complex areas, which cannot be compressed into a 2.5D raster [15]. Areas of geological interest and good exposure, such as cliffs, quarries and mines, often include sub-vertical faces with multiple orientations, so cannot be projected onto a 2D grid (DEM/orthomosaic) without introducing projective distortions that significantly degrade data quality [10,22,23]. To mitigate these issues and facilitate the collection of geological data from potentially important but otherwise inaccessible outcrops, a rapidly increasing number of methods are being developed to fuse remotely sensed hyperspectral data with dense 3D point clouds to create hyperclouds [8,10,17,23,24] that objectively record outcrop geometry and composition.…”

Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

“…Submerged aquatic vegetation (SAV) is vital to the health of aquatic ecosystems. It provides habitat and food for fauna, stabilizes sediments, modifies flow regimes, and improves water quality (Hestir et al, 2016;Shinkareva et al, 2019;United Nations Environment Programme, 2020). SAV is also facing severe threats in the forms of warming waters, increased water levels, invasive species, and human modification to waterways (Massicotte et al, 2015;Zhang et al, 2017;United Nations Environment Programme, 2020).…”