Hyperspectral Endmember Extraction Using Spatially Weighted Simplex Strategy

Xiangfei Shen,Wenxing Bao

doi:10.3390/rs11182147

Xiangfei Shen, Wenxing Bao

Open Access

https://doi.org/10.3390/rs11182147

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Abstract

Spatial information is increasingly becoming a vital factor in the field of hyperspectral endmember extraction, since it takes into consideration the spatial correlation of pixels, which generally involves jointing spectral information for preprocessing and/or endmember extraction in hyperspectral imagery (HSI). Generally, simplex-based endmember extraction algorithms (EEAs) identify endmembers without considering spatial attributes, and the spatial preprocessing strategy is an independently executed module that can provide spatial information for the endmember search process. Despite this interest, to the best of our knowledge, no one has studied the integration framework of the spatial information-embedded simplex for hyperspectral endmember extraction. In this paper, we propose a spatially weighted simplex strategy, called SWSS, for hyperspectral endmember extraction that investigates a novel integration framework of the spatial information-embedded simplex for identifying endmember. Specifically, the SWSS generates the spatial weight scalar of each pixel by determining its corresponding spatial neighborhood correlations for weighting itself within the simplex framework to regularize the selection of the endmembers. The SWSS could be implemented in the traditional simplex-based EEAs, such as vertex component analysis (VCA), to introduce spatial information into the data simplex framework without the computational complexity excessively increasing or endmember extraction accuracy loss. Based on spectral angle distance (SAD) and root-mean-square-error (RMSE) evaluation criteria, experimental results on both synthetic and C u p r i t e real hyperspectral datasets indicate that the simplex-based EEA re-implemented by the SWSS has a significant improvement on endmember extraction performance over the techniques on their own and without re-implementing.

Highlights

Hyperspectral remote sensing is related to the extraction of information from objects or scenes lying on the Earth’s surface, based on their radiance acquired by airborne or spaceborne sensors [1,2]
Despite many studies taking into consideration a nonlinear mixing model (NLMM) that is based on the assumption of physical interactions between the light scattered by multiple materials, the past decades have witnessed a huge growth in the LMM, which assumes that the mixing scale is macroscopic and the incident light interacts with just one material [9]
As the well-known spatial preprocessing strategy, the spatial preprocessing algorithm (SPP) was used to combine with the four original simplex-based algorithms to validate the performance of the improved versions

Summary

Introduction

Hyperspectral remote sensing is related to the extraction of information from objects or scenes lying on the Earth’s surface, based on their radiance acquired by airborne or spaceborne sensors [1,2]. Pure pixel assumption-based algorithms have probably been the most often used in SU owing to their light computational burden and clear conceptual meaning [9]. They generally assume that there is at least one pure pixel per endmember on the vertex to define a data simplex. They generally focus on one of the following geometric properties: (1) the endmember corresponds to an extreme projection on a subspace; or (2) the endmember corresponds to the spectral signatures that can define a maximum simplex volume In this regard, the endmember extraction can be seen as an identification of pure pixels extracted from the HSI.

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Conclusion