Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Li, Chang; Ma, Yong; Mei, Xiaoguang; Fan, Fan; Huang, Jun; Ma, Jiayi

doi:10.3390/rs9111166

Cited by 16 publications

(11 citation statements)

References 63 publications

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“…Hyperspectral images (HSIs) are usually acquired in hundreds of narrow contiguous spectral bands by a specific kind of imaging sensor, e.g., the Airborne Visible/Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment and Compact Airborne Spectrographic Imager [1][2][3]. Due to the high spectral resolution, it is inevitable to bring about the problem of "mixed pixels", and different materials usually occupy a single hyperspectral pixel [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Liu

Cheng

et al. 2018

Remote Sensing

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Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.Keywords: additive white Gaussian noise (AWGN); hyperspectral images (HSIs); mixed noise; bandwise generalized bilinear model (BGBM); alternative direction method of multipliers (ADMM) some flexible models based on signal processing, such as post-nonlinear model [22], neural network model [23] and kernel model [24]. The second category includes some physical based models, such as intimate mixture model [25], bilinear mixture model (BMM) [26][27][28][29][30][31][32][33] and multilinear mixing model [34][35][36]. Among them, the BMM only takes the second-order scattering into consideration, while the higher-order interactions of light are ignored [18]. The reason is that the interactions of orders larger than two, not only have minor contribution for improving the unmixing accuracy than that of second-order scattering, but also bring in tremendous computational costs [21]. Several representative models known as the family of BMM have been proposed. The Nascimento model (NM) [26] is an extended LMM with additional virtual endmembers, the Fan's model (FM) is the truncated Taylor expansion of nonlinear mixing function [27], and the GBM [28] can be seen as the generalization of LMM and FM, which can efficiently deal with the assumptions in BMM. Different methods have been proposed for GBM unmixing of HSI, Halimi et al. developed a Bayesian algorithm to estimate the abundance and noise variance of the GBM [28]. Besides, they also proposed a pixel-wise unmixing method based on the gradient descent algorithm (GDA) [29]. Moreover, Yokoya et al. proposed the semi-nonnegative matrix factorization (semi-NMF) as a new optimization method for GBM based HSI unmixing [30]. Furthermore, Li et al. developed the bound projected optimal gradient method (BPOGM) for GBM unmixng, and it can achieve the optimal convergence rate of O( 1 k 2 ), where k denotes the number of iteration in BPOGM [31].Most unmixing methods based on the GBM are implicitly developed for Gaussian noise, and the underlying assumptio...

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Liu

Cheng

et al. 2018

Remote Sensing

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“…Popular sparse unmixing methods like-variable splitting and augmented Lagrangian (SUnSAL) [30] employed l 1 sparsity term, collaborative SUnSAL algorithm [31] combined collaborative sparse regression with the sparsity promoting term, whereas, SUnSAL-TV [32] introduced a total variation regularization term in the sparse unmixing. Among the sparse unmixing methods for abundance estimation robust sparse unmixing [33,34] method incorporates a redundant regularization term to account for endmember variability, joint local abundance method [35] performs local unmixing by exploiting structural information of image, co-evolutionary approach [36] formulates a multi-objective strategy and minimize it by evolutionary algorithm. Other works such as Feng et al [37] proposed a spatial regularization framework which employs maximum a posteriori estimation, Themelis et al [38] introduced a hierarchical Bayesian model based sparse unmixing method, Zhang et al [39] transform data in framelet domain and maximize the sparsity of the obtained abundance matrix, Zhu et al [40] proposed a correntropy maximization approach for sparse unmixing.…”

Section: Introductionmentioning

confidence: 99%

“…Other works such as Feng et al [37] proposed a spatial regularization framework which employs maximum a posteriori estimation, Themelis et al [38] introduced a hierarchical Bayesian model based sparse unmixing method, Zhang et al [39] transform data in framelet domain and maximize the sparsity of the obtained abundance matrix, Zhu et al [40] proposed a correntropy maximization approach for sparse unmixing. Some recent works such as Li et al [34], Feng et al [41], Mei et al [42] used spatial information alongside spectral properties of the data. Since sparse unmixing consider the whole spectral library as endmember.…”

Section: Introductionmentioning

confidence: 99%

Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA

Das¹,

Routray²,

Deb³

2018

Remote Sensing

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Dictionary pruning step is often employed prior to the sparse unmixing process to improve the performance of library aided unmixing. This paper presents a novel recursive PCA approach for dictionary pruning of linearly mixed hyperspectral data motivated by the low-rank structure of a linearly mixed hyperspectral image. Further, we propose a mutual coherence reduction method for pre-unmixing to enhance the performance of pruning. In the pruning step we, identify the actual image endmembers utilizing the low-rank constraint. We obtain an augmented version of the data by appending each image endmember and compute PCA reconstruction error, which is a convex surrogate of matrix rank. We identify the pruned library elements according to PCA reconstruction error ratio (PRER) and PCA reconstruction error difference (PRED) and employ a recursive formulation for repeated PCA computation. Our proposed formulation identifies the exact endmember set at an affordable computational requirement. Extensive simulated and real image experiments exhibit the efficacy of the proposed algorithm in terms of its accuracy, computational complexity and noise performance.

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“…The proposed method was compared with three state-of-the-art sparse unmixing algorithms: Sparse Unmixing via variable Splitting and Augmented Lagrangian (SUnSAL), Sparse Unmixing method based on Noise Level Estimation (SU-NLE) [49], Sparse Unmixing via variable Splitting and Augmented Lagrangian and Total Variation (SUnSAL-TV), and Non-Local Sparse Unmixing (NLSU). The accuracy assessment of all the experiments in this paper was made by the Signal-to-Reconstruction Error (SRE) [50], which is defined as follows:…”

Section: Experiments and Analysismentioning

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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Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Cited by 16 publications

References 63 publications

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA

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

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