Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery

Feng, Ruyi; Zhong, Yanfei; Zhang, Liangpei

doi:10.1016/j.isprsjprs.2014.07.009

Cited by 29 publications

(7 citation statements)

References 36 publications

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“…These two images represent the same situation and were obtained in the same time period, but they have different spatial and spectral resolutions. To build a dataset for spectral unmixing, geometrical calibration, classification, and down-sampling were undertaken on the HR image to obtain the approximate reference abundance maps [32,50]. The specific process for the HR image was as follows.…”

Section: Experimental Datasetsmentioning

confidence: 99%

“…Facing the above problem, the traditional sparse unmixing approaches usually adopt the alternating direction method of multipliers (ADMM) as the optimization strategy to efficiently obtain the constrained sparse regression [10,19,[24][25][26][32][33][34][35][36][37][38]; for example, sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) [25], non-local sparse unmixing (NLSU) [26], and collaborative SUnSAL (CSUnSAL) [34,35]. Since the ADMM can decompose a difficult problem into a sequence of simpler ones, the original sparse unmixing problem can be solved efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

2018

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Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractional abundances, for the linear regression problem. There are many mathematical techniques that can be used to deal with the spectral unmixing problem; e.g., ordinary least squares (OLS), constrained least squares (CLS), orthogonal matching pursuit (OMP), and basis pursuit (BP). However, due to poor prediction accuracy and non-interpretability, the traditional methods often cannot obtain satisfactory estimations or achieve a reasonable interpretation. In this paper, to improve the regression accuracy of sparse unmixing, least angle regression-based constrained sparse unmixing (LARCSU) is introduced to further enhance the precision of sparse unmixing. Differing from the classical greedy algorithms and some of the cautious sparse regression-based approaches, the LARCSU algorithm has two main advantages. Firstly, it introduces an equiangular vector to seek the optimal regression steps based on the simple underlying geometry. Secondly, unlike the alternating direction method of multipliers (ADMM)-based algorithms that introduce one or more multipliers or augmented terms during their optimization procedures, no parameters are required in the computational process of the LARCSU approach. The experimental results obtained with both simulated datasets and real hyperspectral images confirm the effectiveness of LARCSU compared with the current state-of-the-art spectral unmixing algorithms. LARCSU can obtain a better fractional abundance map, as well as a higher unmixing accuracy, with the same order of magnitude of computational effort as the CLS-based methods.

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Section: Experimental Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

2018

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“…AX − Y 2 F is the data-fitting term, and F denotes the Frobenius norm. X 1,1 = n ∑ j=1 x j 1 , and x j denotes the j-th column of X [40]. The last term, ι R m×n + (X), is aimed at Remote Sens.…”

Section: Spatial Sparse Unmixingmentioning

confidence: 99%

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

et al. 2017

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Abstract:Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. In this paper, to better extract the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (namely, Rolling Guidance Sparse Unmixing (RGSU)) is proposed to extract and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery have different meanings and importance. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The proposed method is based on rolling guidance spatial regularization in a traditional spatial regularization sparse unmixing framework, and it accomplishes scale-aware sparse unmixing. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves visual effects better and produces higher quantitative results (i.e., higher SRE values) when compared to the current state-of-the-art sparse unmixing algorithms, which illustrates the effectiveness of the rolling guidance based scale aware method. In the future work, adaptive scale-aware spatial sparse unmixing framework will be studied and developed to enhance the current idea.

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“…2012; Zhong and Zhang 2014; Feng et al. 2014; Iordache et al. 2011), i.e., by assuming that the endmembers are known in advance.…”

Section: Introductionmentioning

confidence: 99%

Manifold regularization for sparse unmixing of hyperspectral images

Liu

Zhang

et al. 2016

SpringerPlus

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Background Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression, ignore the intrinsic geometric structure in the hyperspectral data.ResultsIn this paper, we propose a novel model, called manifold regularized collaborative sparse regression, by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model.ConclusionsExperimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

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Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery

Cited by 29 publications

References 36 publications

Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Manifold regularization for sparse unmixing of hyperspectral images

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