Superpixel linear independent preprocessing for endmember extraction

One of the limitations of remote sensing is the low spatial resolution of the open-access multispectral sensors, generating a mixture of spatial information. The mixed pixels can be modelled as the linear combination of fundamental components, called endmember, with a weighted contribution or abundance. The development of linear unmixing algorithms considering spatial and spectral information has recently increased. Some unmixing methods have relied on segmentation to integrate spatial data, and one of the most used is superpixel-based segmentation. However, previous work in superpixel-based unmixing focuses on using superpixels as uniform regions. Commonly, linear unmixing is used on hyperspectral imagery, and limited literature is found with multispectral images. This paper aims to propose a new preprocessing approach for multispectral linear unmixing called Superpixel Linear Independent Preprocessing. The proposed approach generates a set of candidates to endmembers based on spatial-spectral information; these are the input of traditional endmember extraction methods for multispectral unmixing. Experimental results show that the proposed preprocessing improves the performance of endmember extraction.