Unmixing Of Hyperspectral Images Research Articles

Improving Reliability in Nonlinear Hyperspectral Unmixing by Multidimensional Structural Optimization

Nonlinear unmixing algorithms are playing a key role in modern earth observation analysis thanks to their ability to characterize complex phenomena occurring in the instantaneous field of view. When unmixing hyperspectral images according to nonlinear mixture models by means of state-of-the-art methods, actual abundances of the elements in the scene can be only indirectly estimated. Thus, the reliability of the investigation can be dramatically jeopardized, hence degrading the accuracy of the characterization of the surface composition. In order to overcome this issue, we propose in this paper a nonlinear programming scheme that aims at providing direct estimation of the end members fractions. The method we introduce is based on a structural optimization approach where the abundances are directly assessed, so that no epistemic uncertainties are injected in the framework. Experimental results show that the proposed method is able to deliver accurate and reliable estimates of these quantities in hyperspectral images.

Bilateral filter based total variation regularization for sparse hyperspectral image unmixing

Spectral unmixing of hyperspectral images aims to find the proportion of constituent materials in mixed pixels. The total variation (TV) regularization is widely included in classical sparse regression formulations to exploit the spatial information in hyperspectral data. It promotes piecewise constant transitions in the fractional abundance of the same endmember among neighboring pixels. The TV regularization term, however, usually brings some staircase effects. To alleviate this drawback, we propose a bilateral filter based TV regularization for hyperspectral image unmixing. Then we present an unmixing model that combines a data-fidelity term, a sparsity regularization term, and the new regularization term. To solve the proposed model, we design an algorithm called sparse unmixing via variable splitting augmented Lagrangian and bilateral filter based TV (SUnSAL-BF-TV), under the alternating direction method of multipliers (ADMM) framework. Our experimental results show that our algorithm is effective to unmix both simulated and real hyperspectral data sets.

Spectral Super Resolution of Hyperspectral Images via Coupled Dictionary Learning

High-spectral resolution imaging systems play a critical role in the identification and characterization of objects in a scene of interest. Unfortunately, multiple factors impair spectral resolution, as in the case of modern snapshot spectral imagers that associate each hyperpixel with a specific spectral band. In this paper, we introduce a novel postacquisition computational technique aiming to enhance the spectral dimensionality of imaging systems by exploiting the mathematical frameworks of sparse representations and dictionary learning. We propose a coupled dictionary learning model which considers joint feature spaces, composed of low- and high-spectral resolution hypercubes, in order to achieve spectral superresolution performance. We formulate our spectral coupled dictionary learning optimization problem within the context of the alternating direction method of multipliers, and we manage to update the involved quantities via closed-form expressions. In addition, we consider a realistic spectral subsampling scenario, taking into account the spectral response functions of different satellites. Moreover, we apply our spectral superresolution algorithm on real satellite data acquired by Landsat-8 and Sentinel-2 sensors. Finally, we have investigated the problem of hyperspectral image unmixing using the recovered high-spectral resolution data cube, and we are able to demonstrate that the proposed scheme provides significant value in hyperspectral image understanding techniques. Experimental results demonstrate the ability of the proposed approach to synthesize high-spectral-resolution 3-D hypercubes, achieving better performance compared to state-of-the-art resolution enhancement methods.

Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction

In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time.

Partially Asynchronous Distributed Unmixing of Hyperspectral Images

So far, the problem of unmixing large or multitemporal hyperspectral data sets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular, relying on the alternating direction method of multipliers. In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in nonconvex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this paper can be implemented to address various matrix factorization problems.

Nonlinear unmixing of hyperspectral images based on double-bird flock optimization

针对高光谱图像像元中端元物质非线性混合的特点，借鉴生物群智能现象，提出一种基于双鸟群优化的高光谱图像非线性解混算法。为进一步提高非线性解混算法的精度，通过模拟鸟群中觅食、警惕以及飞行等行为得到非线性问题的最优解。算法通过双鸟群的迭代优化来交替更新目标函数中的最优解以及非线性模型参数，最终得到高光谱图像端元丰度的最佳估计。仿真实验和光谱数据实验结果表明:双鸟群优化算法迭代收敛，能克服局部最小值问题；相比于同类算法，该算法解混结果的丰度重建误差、平均光谱角距离和像元重建误差3项指标均较小，该算法解混精度高，像元重构效果好，能有效提高高光谱图像非线性解混的精度。

Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis

Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and β-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively.

A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images

Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo3 framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo3-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth.

N-PSO: endmember extraction using advance particle swarm optimization for NLMM

This paper presents a fully unsupervised endmember extraction technique for hyperspectral image unmixing using nonlinear mixing model. The underlying idea of the model is that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive noise. These nonlinear functions are approximated using polynomial functions, leading to a polynomial post-nonlinear mixing model (PPNM). In an unknown environment, the evaluation of the parameters involved in PPNM model is a tedious task, which is categorized as an NP hard problem. A method based on the combination of swarm intelligence, least-square (LS) and sub-gradient-based optimization (SO) is proposed to estimate the parameters involved in the model. The particle swarm optimization (PSO) is used to search the optimal endmember combination in the feasible solution space. The nonlinearity and respective abundances are evaluated using the LS and SO method, respectively. The proposed method is equipped with an adaptive tuning parameter-free mechanism and modified updating strategy. This strategy not only improves the result in terms of overall accuracy but also maintains physical constraints on the value of the resultant endmember set. The proposed method has been evaluated using simulated and real hyperspectral scenes. The experimental results on the hyperspectral scenes show that the proposed method obtains a higher extraction precision than those of the existing endmember extraction algorithms. Statistical analysis on a real hyperspectral image shows that the results obtain using N-PSO are 20–40% better than those from the existing approaches.

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

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.

A sparsity-based Bayesian approach for hyperspectral unmixing using normal compositional model

A new Bayesian-based method is developed for unmixing of hyperspectral images. Endmembers are assumed variable based on the Gaussian distribution. A semi-supervised scenario is considered, and as a practical aspect, the abundance vectors are assumed sparse. We propose the Dirichlet prior to represent the sparsity and derive the corresponding posteriors in Bayesian sense. Numerical results are used to evaluate different methods for both simulated and real data. It is shown that the proposed method achieves a lower error in abundance estimation and image reconstruction.

基于高光谱图像解混的海洋绿藻检测算法

基于高光谱图像解混的海洋绿藻检测算法

高光谱图像Pareto优化稀疏解混

高光谱图像Pareto优化稀疏解混

Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation

Abstract Generalized bilinear model (GBM) has been one of the most representative models for nonlinear unmixing of hyperspectral image (HSI), which can take the second-order scattering of photons into consideration. However, the GBM is implicitly developed for the additive white Gaussian noise. Besides, the performances of traditional GBM based unmixing methods are not that satisfying since the spatial correlation of HSI is not considered. In this paper, to overcome the two problems mentioned above, we propose a robust GBM (RGBM) for nonlinear unmixing of HSI, which can simultaneously take the Gaussian noise and sparse noise into account. Besides, we propose a new unmixing method with superpixel segmentation (SS) and low-rank representation (LRR) based on RGBM, which can take the spatial correlation of HSI into consideration. First, we adopt the principal component analysis (PCA) to get the first principal component of HSI, which contains the most information for the whole HSI. Then we adopt the SS in the first principal component of HSI to get the homogeneous regions, and the abundances in each homogeneous region have the underlying low-rank property. Finally, we unmix the pixels in each homogeneous region of HSI according to the low-rank property of abundances and the sparse property of sparse noise, and the proposed RGBM based unmixing method can be solved by the alternative direction method of multipliers (ADMM). Experiments on both synthetic datasets and real HSIs demonstrate that the proposed RGBM and corresponding method are efficient compared with some other popular GBM based unmixing methods.

Fast FPGA Implementation for Computing the Pixel Purity Index of Hyperspectral Images

The pixel purity index (PPI) algorithm is one of the most popular endmember extraction algorithms employed in hyperspectral image unmixing, which is too time-consuming to obtain real-time analysis in remote sensing applications. The fast field programmable gate array (FPGA) implementation for computing the PPI is proposed in this reported work. The parallel strategy by skewers consumes lower I/O bandwidth and on-chip memory capacity, and the Xilinx Vivado high-level-synthesis (HLS) tool speeds up our architecture design and implementation. The overall design can be simple to implement, and makes the FPGA hardware appealing for on-board hyperspectral unmixing.

Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing

Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e., the intraclass variability of the materials. The former limitation has been addressed by designing nonlinear mixture models, whereas the second can be dealt with by using (usually linear) space varying models. The typical example is a linear mixing model where the sources can vary from one pixel to the other. In this letter, we show that a recent variability model can also estimate the abundances of nonlinear mixtures to some extent. We make the theoretical connection between nonlinear models and this variability model, and confirm it with experiments on nonlinearly generated synthetic datasets.

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

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.

Detection of blood in fish muscle by constrained spectral unmixing of hyperspectral images

A new method for blood detection in whitefish fillets is presented. The method is based on diffuse reflectance hyperspectral imaging in the VIS/NIR range, and unmixing of measured absorbance spectra into known spectra for hemoglobin, water and muscle tissue. Scattering effects are modelled as a polynomial function of wavelength, and are included in the unmixing procedure. The unmixing method is based on a modified Beer-Lambert law and is performed using a non-negativity constrained least squares algorithm. Application of the method to images of samples with varying amounts of blood results in a very good fit between measured and modelled spectra. Reference measurements on homogenized cod muscle with controlled concentrations of added blood are used to model the relationship between the spectral unmixing results and hemoglobin concentration, enabling pixel-by-pixel estimation of hemoglobin concentration. The method is seen as a useful tool for automatic quality grading and processing of fillets in the whitefish industry.

FC-TLBO: fully constrained meta-heuristic algorithm for abundance estimation using linear mixing model

A study of abundance estimation has vital importance in spectral unmixing of hyperspectral image. Recently, various methods have been proposed for spectral unmixing to achieve higher performance using an evolutionary approach. However, these methods are based on unconstrained optimisation problems. Their performance was also based on proper tuning parameters. We have proposed a new non-parametric algorithm using teaching-learning-based optimisation technique with an inbuilt constraints maintenance mechanism using the linear mixing model. In this approach, the unmixing problem is transformed into a combinatorial optimisation problem by introducing abundance sum to one constraint and abundance non-negative constraint. A comparative analysis of the proposed algorithm is conducted with other two state-of-the-art algorithms. Experimental results in known and unknown environments with varying signal-to-noise ratio on simulated and real hyperspectral data demonstrate that the proposed method outperforms the other methods.

Dictionary-Based Tensor Canonical Polyadic Decomposition

To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.