Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach [PDF]
Endmember extraction algorithms (EEAs) are among the most commonly discussed types of hyperspectral image processing in the past three decades. This article proposes a spatial energy prior constrained maximum simplex volume (SENMAV) approach for spatial ...
Xiangfei Shen, Wenxing Bao, Kewen Qu
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Maximum simplex volume: an efficient unsupervised band selection method for hyperspectral image
Hyperspectral imaging makes it possible to obtain object information with fine spectral resolution as well as spatial resolution, which is beneficial to a wide array of applications. However, there is a high correlation among the bands in a hyperspectral
Xuefeng Jiang +3 more
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Parallelotopes of Maximum Volume in a Simplex [PDF]
For a \(d\)-simplex \(S\subset E^d\), denote by \(v_1,\dots, v_d\) the vectors determining the edges of \(S\) starting at its vertex \(0\), say. It is clear that the \(d\)-parallelotope \(P\) with vertex \(0\) and edges at \(0\) determined by the vectors \({1\over d}v_1,\dots, {1\over d}v_d\) is a subset of \(S\) and has \(d!/d^d\) times the volume of \
Marek Lassak
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Spatial Potential Energy Weighted Maximum Simplex Algorithm for Hyperspectral Endmember Extraction
Most traditional endmember extraction algorithms focus on spectral information, which limits the effectiveness of endmembers. This paper develops a spatial potential energy weighted maximum simplex algorithm (SPEW) for hyperspectral endmember extraction,
Meiping Song +3 more
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Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization [PDF]
Consider a structured matrix factorization model where one factor is restricted to have its columns lying in the unit simplex. This simplex-structured matrix factorization (SSMF) model and the associated factorization techniques have spurred much interest in research topics over different areas, such as hyperspectral unmixing in remote sensing, topic ...
Chia-Hsiang Lin +4 more
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Endmember extraction (EE) is a prerequisite task for spectral analysis of hyperspectral imagery. In all kinds of EE algorithms, maximum simplex volume-based ones, such as simplex growing algorithm (SGA) and N-FINDR algorithm, have been widely used for their fully automated and efficient performance.
Liguo Wang +3 more
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Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression [PDF]
Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances.
Behnam Parsaeifard +3 more
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Maximum Simplex Volume based Landmark Selection for Isomap [PDF]
Since traditional linear feature extraction methods are unable to handle nonlinear characteristics often exhibited in hyperspectral imagery, nonlinear feature extraction, also known as manifold learning, is receiving increased attention in hyperspectral remote sensing society as well as other community.
Junhwa Chi
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The Maximum of the Volume of a Cevian Simplex and its Parts [PDF]
The cevian triangle corresponding to an interior point $M$ of a triangle is the triangle determined by the feet of the three cevians concurrent at $M$. It is known that the area of the cevian triangle for an interior point $M$ of a triangle is at most $\frac{1}{4}$ of the area of the triangle, with maximum attained when $M$ is the triangle's centroid ...
Yagub N. Aliyev
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The Maximum of the Volume of a Part of a Cevian Simplex [PDF]
The cevians passing through a point in a simplex create a cevian simplex, which is divided by these cevians into smaller simplices. We consider the problem about the maximum of the ratio of the sum of the volumes of some of these smaller simplices by the volume of the reference simplex. The special case of tetrahedron is given as an example.
Zamina Guliyeva, Yagub N. Aliyev
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