Triple shadow multilinear unmixing for near-ground hyperspectral vegetation canopy shadow removal

Abstract

High spatial resolution hyperspectral images are full of spatial and spectral information, which is beneficial to the identification and extraction of small changes in vegetation. The shape of spectra in the vegetation canopy region is changed by illumination and shadow, which affects the accuracy of parameter retrieval and environmental monitoring. However, existing shadow removal methods primarily target buildings, whose three-dimensional structure differs from that of the vegetation canopy. There is a requirement for research on methods of shadow removal for near-ground hyperspectral vegetation canopy regions, as existing models also suffer from over-compensation and mis-compensation when applied to these areas. Therefore, this study proposed a triple shadow multilinear mixing model-based shadow removal method (triple-SMLM). The triple-SMLM method contains three steps. For the first unmixing, the extraction of vegetation and soil endmembers from the sunlit regions served as input for the SMLM model to detect shadowed regions. For the second unmixing, the shadowed endmembers were selected using the Euclidean distance and unmixed based on the SMLM model. The shadowed regions were divided into two parts based on their abundance and vegetation pixel extraction index (VPEI): information-rich and information-poor shadowed regions. For the third unmixing, the former was compensated by spectral reconstruction based on the unmixed parameters of the previous step, while the latter was compensated by spectral reconstruction in combination with the VPEI indices after unmixing in the SMLM model. The results of the experiment are as follows: (1) The accuracy evaluation shows that the STD and RMSE of the shadow-removed image for the triple-SMLM method are 0.47 and 4.93%, and the results are more precise. (2) Compared with other methods, there is a great improvement in the shadowed boundaries of vegetation and the information-poor shadowed regions of the soil. The shadow removal performance of the triple-SMLM has potential in the vegetation canopy region of near-ground hyperspectral imagery.