Hyperspectral Unmixing Using Spectral Library Sparse Scaling and Guided Filter

Zhang, Zuoyu; Liao, Shouyi; Hao, Fang; Zhang, Hexin; Wang, Shicheng

doi:10.1109/lgrs.2020.3025920

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past few years, several algorithms have been developed to enforce the sparsity on the solution of SU [14], [15]. The sparse unmixing algorithm via variable splitting and augmented Lagrangian (SUnSAL) [9] adopts the L 1 regularizer on the abundance matrix to measure the sparsity of the abundance vector in each pixel.…”

Section: Introductionmentioning

confidence: 99%

Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

Zhang,

Mei,

et al. 2021

Preprint

View full text Add to dashboard Cite

Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis. However, the influence of light, acquisition conditions and the inherent properties of materials, results in that the identified endmembers can vary spectrally within a given image (construed as spectral variability). To address this issue, recent methods usually use a priori obtained spectral library to represent multiple characteristic spectra of the same object, but few of them extracted the spectral variability explicitly. In this paper, a spectral variability augmented sparse unmixing model (SVASU) is proposed, in which the spectral variability is extracted for the first time. The variable spectra are divided into two parts of intrinsic spectrum and spectral variability for spectral reconstruction, and modeled synchronously in the SU model adding the regular terms restricting the sparsity of abundance and the generalization of the variability coefficient. It is noted that the spectral variability library and the intrinsic spectral library are all constructed from the In-situ observed image. Experimental results over both synthetic and real-world data sets demonstrate that the augmented decomposition by spectral variability significantly improves the unmixing performance than the decomposition only by spectral library, as well as compared to state-of-the-art algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%