A Probabilistic Joint Sparse Regression Model for Semisupervised Hyperspectral Unmixing

Seyyedsalehi, Seyyede Fatemeh; Rabiee, Hamid R.; Soltani-Farani, Ali; Zarezade, Ali

doi:10.1109/lgrs.2017.2649418

Cited by 13 publications

(2 citation statements)

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“…Tang et al [58] presented a new algorithm, which is termed sparse unmixing using spectral a priori information, to tackle the noise in observed spectral vectors and high mutual coherence of spectral libraries. Seyyedsalehi et al [59] proposed a probabilistic sparse regression method for linear hyperspectral unmixing, which utilizes the implicit relations of neighboring pixels. For HSIs, low spatial and spectral resolution will cause inaccurate unmixing.…”

Section: Sparse Unmixing Algorithmmentioning

confidence: 99%

An Overview on Linear Unmixing of Hyperspectral Data

Wei

Wang

2020

Mathematical Problems in Engineering

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Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.

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Section: Sparse Unmixing Algorithmmentioning

confidence: 99%

An Overview on Linear Unmixing of Hyperspectral Data

Wei

Wang

2020

Mathematical Problems in Engineering

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“…The linear mixed model has clear physical meaning and ideal effects, when it simulated a mixed pixel composed of multiple targets evenly distributed. Now, many scholars had achieved good academic achievements, who used linear model to research the mixed pixels of soil, vegetation, minerals and water sources [8] [9]. In addition, with the deepening application of hyper-spectral in the remote sensing field , the high-resolution quantitative remote sensing has shown increasing advantages in environmental estimation, resource survey, and crop yield estimation [10] [11].The spectral resolution of the target hyper-spectral data is very high.…”

Section: Introductionmentioning

confidence: 99%

Analysis and optimization method of influencing factors for three-dimensional target mixed pixels under zero meteorological view distance

Wang,

LIU,

CHEN

et al. 2023

Preprint

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Mixed pixels widely exist in the obtained hyper-spectral data,which directly affects the accuracy of optical remote sensing imaging simulation. Aiming at the three-dimensional target under zero meteorological view distance, this paper comprehensively considers the effects of directional reflection characteristics, geometric shadows and background radiation on the generation of mixed pixels.It proposes a method of modeling mixed pixels by taking each surface of a three-dimensional target as an end element component and using the weight coefficient of the distance between the end element groups to the view center. Through designing experiments to simulate different influence conditions such as directional reflection, geometric shadow and background radiation, this paper verifies the influence of the radiation value of the mixed pixels of the three-dimensional target produced by different mixing conditions in the single-pixel field of view. The experimental results show that directional reflection characteristics, geometric occlusion shadows and background radiation have significant effects on the generation of mixed pixels for the three-dimensional targets. The improved mixed pixel modeling method proposed in this paper considering the influence of multiple factors has an average error of less than 10% in modeling accuracy of three-dimensional targets, which is significantly better than traditional linear modeling methods. It can be seen that the research results of this paper have good reference significance for the research of the three-dimensional target mixed pixel radiation model.

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