Abstract
We examined the dependency of the pixel reflectance of hyperspectral imaging spectrometer data (HISD) on a normalized total insolation index (NTII). The NTII was estimated using a light detection and ranging (LiDAR)-derived digital surface model (DSM). The NTII and the pixel reflectance were dependent, to various degrees, on the band considered, and on the properties of the objects. The findings could be used to improve land cover (LC)/land use (LU) classification, using indices constructed from the spectral bands of imaging spectrometer data (ISD). To study this possibility, we investigated the normalized difference vegetation index (NDVI) at various NTII levels. The results also suggest that the dependency of the pixel reflectance and NTII could be used to mitigate the shadows in ISD. This project was carried out using data provided by the Hyperspectral Image Analysis Group and the NSF-funded Centre for Airborne Laser Mapping (NCALM), University of Houston, for the purpose of organizing the 2013 Data Fusion Contest (IEEE 2014). This contest was organized by the IEEE GRSS Data Fusion Technical Committee.
Highlights
Passive remote sensing (RS) is based on the assumption that solar energy reflected from objects varies in intensity across spectral bands as a function of the physicochemical properties of these objects
In this report we summarize our investigations of the relationship between the pixel intensity of 144 bands of an imaging spectrometer data (ISD) set for 15 land use/land cover classes, in addition to the impact of our findings on remote sensing (RS) studies
This finding indicated that the topography of the site was relatively featureless and that the lower values of normalized total insolation index (NTII) were caused by shadows cast by elevated objects, including buildings
Summary
Passive remote sensing (RS) is based on the assumption that solar energy reflected from objects varies in intensity across spectral bands as a function of the physicochemical properties of these objects. It is known that the properties of the reflected energy depend on its incidence angle in respect to the surface of the object reflecting it The impact of this effect on the classification of imaging spectrometer data (ISD) sets has been addressed in several studies (Henrich et al, 2014, Teillet, 1982, Meyer et al, 1993, Sandmeier, et al, 1997, Richter, 1998, Riaño, et al, 2003, Conese, et al, 1993, Civco, 1989, Chavez, 1996). The performance of topography-correction methods for selected types of ISD data sets and selected types of land cover (LC) has been reported (Riaño, et al, 2003, Shepherd, et al, 2003, Alonzo, et al, 2014) These methods use digital surface models (DSMs) to estimate the incidence angle of the solar irradiation. The accuracy of these DEMs varies from approximately 0.15 m (LiDAR) and 2.0 m (SRTM) to 7.0 m (ASTER v2.0) (Becek, 2014)
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