Spectral Unmixing via Data-Guided Sparsity

Zhu, Fayan; Wang, Ying; Fan, Bin; Xiang, Shiming; Meng, Geofeng; Chen, Pan

doi:10.1109/tip.2014.2363423

Cited by 232 publications

(164 citation statements)

References 47 publications

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“…Samson data is a hyperspectral data owned by Oregon State University provided by WeoGeo [71], which is a push broom visible to near infrared sensor. The pixel responses are captured by 156 bands in the spectral range of 401 nm-889 nm with resolution up to 3.13 nm.…”

Section: Real Data Samson Datamentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Section: Real Data Samson Datamentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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“…The spatial information could be incorporated with the L 2 sparse constraint in order to achieve a more accuracy result. Similar to [30] and [31], a pixel-wise or region-smart L 2 sparse constraint might be developed.…”

Section: Discussionmentioning

confidence: 99%

“…Some additional techniques were introduced to further utilize the sparsity of the abundances more subtly, such as the data-guided sparsity [30], reweighted L 1 -norm regularizer [34], and the arctan sparse constraint [44]. In this paper, we considered the ASC and the sparse constraint together, and proposed to use the L 2 -norm in order to enhance the sparsity of the abundances in the NMF model.…”

Section: Sparse Priormentioning

confidence: 99%

“…Recently, some researchers have incorporated spatial information with the sparseness constraint. Zhu et al has proposed a data-guided sparse method, in which the value of q for each pixel is determined by a learned data-guided map in order to adaptively impose the sparseness constraint [30]. A similar idea was adopted by Wang et al, as they incorporated a spatial group sparseness constraint into NMF for HU, which supposes that the pixels within a local spatial group share the same sparse structure [31].…”

Section: Introductionmentioning

confidence: 99%

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Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

et al. 2018

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Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization (NMF) have been proved effective for HU, which usually uses the sparsity of abundances and the correlation between the pixels to alleviate the non-convex problem. However, the commonly used L 1/2 sparse constraint will introduce an additional local minima because of the non-convexity, and the correlation between the pixels is not fully utilized because of the separation of the spatial and structural information. To overcome these limitations, a novel bilateral filter regularized L 2 sparse NMF is proposed for HU. Firstly, the L 2 -norm is utilized in order to improve the sparsity of the abundance matrix. Secondly, a bilateral filter regularizer is adopted so as to explore both the spatial information and the manifold structure of the abundance maps. In addition, NeNMF is used to solve the object function in order to improve the convergence rate. The results of the simulated and real data experiments have demonstrated the advantage of the proposed method.

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“…Jasper Ridge is one of the popular datasets used in hyperspectral data analysis [11]- [12]. Jasper Ridge is a cube of data consists of 512 rows × 614 columns × 224 bands.…”

Section: Real Dataset (Jasper Ridge) IVmentioning

confidence: 99%

New Divide and Conquer Method on Endmember Extraction Techniques

Samir¹,

Abdellatif²,

Badr³

2017

ijacsa

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Abstract-In hyperspectral imagery, endmember extraction (EE) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature, endmembers, from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data. EE process has some difficulties. There are less distinct endmembers than its mixed background; also, there are endmembers that have rare occurrences in data that are considered as difficulties in EE process. In this paper, we propose a new technique that uses divide and conquer method for EE process to find out these difficult (rare or less distinct) endmembers. divide and conquer method is used to divide hyperspectral data scene to multiple divisions and take each division as a standalone scene to enable endmember extraction algorithms (EEAs) to extract difficult endmembers easily and finally conquer all extracted endmembers from all divisions. We implemented this method on real dataset using three EEAs: ATGP, VCA, and SGA and recorded the results that outperform the results from usual endmember extraction techniques methods in all used algorithms.

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Spectral Unmixing via Data-Guided Sparsity

Cited by 232 publications

References 47 publications

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

New Divide and Conquer Method on Endmember Extraction Techniques

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