Abstract
Abstract. The objective of this study was to evaluate the impact of reducing the radiometric information of hyperspectral images. The image data was collected originally with 32 bits and rescaled to 8 and 16 bit/pixel. The images were acquired with a Rikola Hyperspectral Camera attached to an Unmanned Aerial Vehicle (UAV). After the geometric and radiometric processing of the images, a mosaic was obtained with pixels representing reflectance factor coded in 32 bits. Using the minimum and maximum values of each spectral band, a linear equation was thus applied to reduce the radiometric resolution of the original mosaic, from 32 bits to 8 bits and from 32 bits to 16 bits. Following, the Normalized Root Mean Square Error (NRMSE %) and the Mean Absolute Percentage Error (MAPE %) were used to evaluate the results, showing that for the 8 bits mosaic, the loss of information was higher. For this radiometric resolution rescaling, the MAPE % achieved values until 22.486 % and the highest NRMSE % value was 0.455 % while, for the 16 bits mosaics, the highest MAPE % and NRMSE % values were 0.069 % and 0.002 %, respectively. Finally, it can be inferred that the impact of radiometric transformation can be considered as negligible for the hyperspectral mosaic with 16 bits of radiometric resolution, which presented lower values of NRMSE % and MAE % and could not affect the mosaic analysis.
Highlights
The use of Unmanned Aerial Vehicle (UAV) remote sensing is expected to revolutionize various environmental applications due to their capability to capture datasets at desired spatial, spectral, radiometric and temporal resolutions (Anderson and Gaston, 2013; Sanchez-Azofeifa et al, 2017)
The objective of this study is to evaluate the impact of reducing the radiometric information of hyperspectral images comparing the data with 32 bits of a mosaic composed by hyperspectral images with those obtained by rescaling to 8 and 16 bits
These results showed different error magnitudes, which may affect the MAPE% values
Summary
The use of Unmanned Aerial Vehicle (UAV) remote sensing is expected to revolutionize various environmental applications due to their capability to capture datasets at desired spatial, spectral, radiometric and temporal resolutions (Anderson and Gaston, 2013; Sanchez-Azofeifa et al, 2017). The radiometric resolution of a sensor is an important characteristic to be explored since it reflects the quantization level that the sensor is capable to record and represent the reflected energy of targets, besides influencing the analysis of targets spectral features (Orych et al, 2014; Tucker, 1980). This subject is not commonly studied, it is of wide interest for the research community and practical applications, especially when hyperspectral images with high spatial resolution are used. Some level of radiometric resolution reduction should be interesting
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