This paper proposed a new manner to approximate the sparsity and low rankness penalties in the unmixing model. The core of the proposed framework is to design ...
In this paper, we propose a new framework called hyper- spectral sparse unmixing via firm thresholding mapping. (HSUFTM). The proposed method provides a new man ...
The heart of the proposed framework is to design the new nonconvex penalties for efficient minimization by the means of two families of thresholding mappings, ...
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What is hyperspectral unmixing?
What is sparse unmixing?
We propose a novel sparse unmixing method based on the bandwise model (SUBM). Abstract. Sparse unmixing has long been a hot research topic in the area of ...
Missing: Firm | Show results with:Firm
Oct 31, 2021 · Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and ...
In this paper, we will present preliminary results on how unsupervised hyperspectral unmixing algorithms can be used to extract spectral signatures of materials ...
Missing: Firm Thresholding
In this study, we compare four unmixing algorithms with the ultimate goal of analyzing their potential in solving sparse hyperspectral unmixing problems. The ...
Missing: Firm | Show results with:Firm
Abstract—Spectral variation, which is inevitably present in hyperspectral data due to nonuniformity and inconsistency of illu-.
A convex framework for SU that incorporates the group structure of the spectral library and demonstrates the superiority and effectiveness of the proposed ...
In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse ...