Abstract. Hyperspectral imaging is crucial for a variety of land-cover mapping and analyzing tasks. The available large number of reflected light measurements along a wide range of wavelengths allows for distinguishing between different materials under various conditions. Though, several effects bear an undesired variability within hyperspectral images and increase the complexity of interpreting such data. Two of the most significant effects in this regard are the BRDF and the spectral mixture. Due to the first, the acquisitions geometrical and viewing conditions influences the measured spectral signature of a surface to a large extent. On the other hand, because of the typical low spatial resolution of remotely sensed images, each pixel can contain more than one material. Despite much research addressing either the BRDF effect and ways to correct it or the spectral unmixing, too few works considered these two effects' mutual influence. In this work, we study the BRDF of mixed pixels and present preliminary insights of testing a strategy to correct its undesired impact on the data by incorporating the EMs fractions within an unmixing-based semi-empirical correction model. Experimental results using real laboratory data acquired under controlled conditions clearly show the significant improvement of the corrected reflectance results through the proposed model.
Abstract. The spectral mixture analysis (SMA) plays a vital role in spectral data analysis and extraction of subpixel information. However, this technique provides only quantitative information regarding the materials’ abundance fractions within the pixel. On the other hand, the Bidirectional Reflectance Distribution Function (BRDF) indicates that sub-pixel topography affects the surface’s directional reflection to a large extent. Unfortunately, despite the high importance of the BRDF effect and the SMA in remote sensing, only very few research works addressed their mutual influence. Thus, in this work, we propose a study that addresses this mutual influence and suggests an approach for extracting sub-pixel topographic information from mixed pixels. For this purpose, we conducted two multiview imaging experiments under controlled conditions using artificial mixed surfaces. Each surface type is made of two materials and has a varying structural pattern. Then we measured the BiConical Reflectance Factor (BCRF) of each surface from various viewing zenith angles. Next, we applied spectral unmixing to estimate the abundance fraction of three endmembers (EMs) in each surface’s pattern. Finally, we tested the relationship between the sup-pixel topography and the fraction variation vs. the multiple imaging directions. The first experiment results showed that multiview spectral measurements allow the separability between surfaces combining the same materials’ composition but with different sub-pixel structural arrangements.Moreover, such separability is more accurate in the fraction space than in reflectance space. Besides, and most importantly, the second experiment revealed exciting outcomes regarding the relationship between the sub-pixel topographic feature and the variation of the EM fraction vs. the imaging viewing direction. Specifically, we showed a high correlation between the EMs’ fractions and the height of a repetitive element within the sub-pixel topography with a determination coefficient that reaches 0.89.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.