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Hyperspectral Image Endmember Extraction Algorithm Based on Manifold Learning and Superpixel Feature Extraction | IEEE Conference Publication | IEEE Xplore

Hyperspectral Image Endmember Extraction Algorithm Based on Manifold Learning and Superpixel Feature Extraction


Abstract:

Endmember extraction is the crucial process of hyperspectral unmixing. In this study, we introduce a nonlinear endmember extraction algorithm for hyperspectral remote sen...Show More

Abstract:

Endmember extraction is the crucial process of hyperspectral unmixing. In this study, we introduce a nonlinear endmember extraction algorithm for hyperspectral remote sensing image analysis. The algorithm use the manifold learning technique to acquire the intrinsic nonlinear structure of hyperspectral data, and extract the endmembers based on convex simplex theory in low-dimensional manifolds. In order to achieve better manifold representations and reduce the computational burden, superpixel feature extraction is performed on the original hyperspectral data. Experimental results on synthetic and real data demonstrate the effectiveness of the proposed method.
Date of Conference: 10-12 May 2024
Date Added to IEEE Xplore: 09 September 2024
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Conference Location: Guangzhou, China

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I. Introduction

Hyperspectral remote sensing images (HRSI) can merge the spatial and spectral information into data cubes from imaged scenes, allowing different land cover objects to be distinguished, and are widely used in geological exploration, environmental monitoring, vegetation investigation and other fields[1]. However, due to the restricted relationship between spectral resolution and spatial resolution, mixed pixel almost exists in every HRSI, and has been a key issue of hyperspectral image processing. Hyperspectral unmixing (HU) is the most effective techniques to address mixed pixel problems. HU aims to decompose spectral signature of the mixed pixel into a set of pure constituent spectra, called endmembers, and a collection of corresponding abundance fractions [2].

References

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