- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Reweighted Local Collaborative Sparse Regression for Hyperspectral Unmixing
摘要： Sparse unmixing is based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combinations of known pure signatures in the spectral library. Collaborative sparse regression improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. However, hyperspectral images exhibit rich spatial correlation that can be exploited to better estimate endmember abundances. The work, based on the iterative reweighted algorithm and local collaborative sparse unmixing, utilized a reweighted local collaborative sparse unmixing (RLCSU). The simultaneous utilization of iterative reweighted minimization and local collaborative sparse unmixing (including spectral information and spatial information in the formulation, respectively) significantly improved the sparse unmixing performance. The optimization problem was simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results were obtained by using both simulated and real hyperspectral data sets. The proposed RLCSU algorithm obtain better signal-to-reconstruction error (SRE, measured in dB) results than LCSU and CLSUnSAL algorithms in all considered signal-to-noise ratio (SNR) levels, especially in the case of low noise values. The RLCSU algorithm obtains a better SRE(dB) result (30.01) than LCSU (20.08) and CLSUnSAL (17.28) algorithms for the simulated data 1 with SNR=50dB. It demonstrated that the proposed method is an effective and accurate spectral unmixing algorithm.
关键词： Hyperspectral unmixing,spectral unmixing,reweighted local collaborative,spatial information,sparse regression
更新于2025-09-23 15:23:52
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Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction
摘要： 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.
关键词： hyperspectral imagery,spectral unmixing,Constraints,nonnegative tensor factorization
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Blind Nonlinear Hyperspectral Unmixing Using an <tex>$\ell_{q}$</tex> Regularizer
摘要： Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an lq regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.
关键词： non-negative matrix factorization,Spectral unmixing,bilinear model,hyperspectral images
更新于2025-09-23 15:22:29
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Loss of target information in full pixel and subpixel target detection in hyperspectral data with and without dimensionality reduction
摘要： In most hyperspectral target detection applications, targets are usually small and require both spatial as well as spectral detection. Hyperspectral imaging facilitates target detection (TD) applications greatly, however, due to large spectral content, hyperspectral data requires dimensionality reduction (DR) which also leads to loss of target information both at full pixel and subpixel level. Literature reports many DR and TD algorithms in practice. Several studies have focussed on assessing the loss of target information in DR, however, not much work seems to have been done to assess loss of target information in full pixel and subpixel TD in hyperspectral data with and without DR. This paper seeks to study various combinations of DR techniques combined with full pixel and subpixel TD algorithms. The results indicate that in the case of full pixel targets, both DR and TD contribute to the loss of target information, however, there is more loss of target information in the case when DR precedes TD in comparison to a case where TD is applied without DR. In the case of subpixel TD, however, there appears to be loss of subpixel target information in the case where TD alone is performed in comparison to a case where DR precedes TD.
关键词： Spectral unmixing,Mixed pixel,Subpixel target detection,Target information,Dimensionality reduction,Full pixel target detection
更新于2025-09-19 17:15:36
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[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - Correlations between generation output of two different photovoltaic power stationsa??a??based on the field data of Xinjiang
摘要： This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies, and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.
关键词： unsupervised spectral unmixing,Hyperspectral imagery,MCMC,Bayesian estimation,anomaly detection
更新于2025-09-19 17:13:59
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Impact of depth-dependent optical attenuation on wavelength selection for spectroscopic photoacoustic imaging
摘要： An optical wavelength selection method based on the stability of the absorption cross-section matrix to improve spectroscopic photoacoustic (sPA) imaging was recently introduced. However, spatially-varying chromophore concentrations cause the wavelength- and depth-dependent variations of the optical fluence, which degrades the accuracy of quantitative sPA imaging. This study introduces a depth-optimized method that determines an optimal wavelength set minimizing an inverse of the multiplication of absorption cross-section matrix and fluence matrix to minimize the errors in concentration estimation. This method assumes that the optical fluence distribution is known or can be attained otherwise. We used a Monte Carlo simulation of light propagation in tissue with various depths and concentrations of deoxy-/oxy-hemoglobin. We quantitatively compared the developed and current approaches, indicating that the choice of wavelength is critical and our approach is effective especially when quantifying deeper imaging targets.
关键词： Spectroscopic photoacoustic imaging,Spectral unmixing,Optimal wavelength selection,Oxygen saturation estimation
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Dataset with Ground-Truth for Hyperspectral Unmixing
摘要： Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure material spectra and material compositions are known. Lab-made hyperspectral datasets with these scenes are then provided, and mutually validated with typical linear and nonlinear unmixing algorithms.
关键词： unmixing database,spectral unmixing,Hyperspectral imaging,ground-truth
更新于2025-09-10 09:29:36
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Unsupervised Nonlinear Spectral Unmixing of Satellite Images Using the Modified Bilinear Model
摘要： Episodes of mixing pixels in satellite imageries are more prevalent. Hence, spectral unmixing approach is used to perform the sub-pixel classification of satellite images. Many unmixing works were done based on the assumption that the pixels are linearly mixed (single interaction) but in real scenarios, the pixels are nonlinearly mixed due to interactions. Fan model and generalized bilinear model consider only the bilinear interactions for nonlinear unmixing. In reality, multiple interactions between the various classes are also present in the image. In this work, a new model, ‘modified bilinear model’ is proposed to perform the nonlinear unmixing process that considers the entire single, bilinear and multiple interactions into account. This system adaptively changes the mixing model on per pixel basis depending on the nonlinearity parameter. It has been tested with the multispectral, synthetic and real hyperspectral datasets and also illustrated notable advantages compared with the other methods.
关键词： Multiple interaction,Spectral unmixing,Endmember extraction,Nonlinear unmixing
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Relative Attribute Based Unmixing
摘要： The abundance of a mixed pixel of certain class can be understood as to get the relative score referring to the pure representative of this class, while not be classified with two absolute and discrete value as “1 or 0”. This is in accordance with the Relative Attribute Learning (RAL) problem in computer vision. In RAL, the concept of “relative attribute” is used to describe the belonging level of an object to certain class with a score which is achieved from a learn-to-rank problem using rankSVM framework. To utilize information between data samples and even of mixed pixels, Relative Attribute based Unmixing (RAU) is proposed first time by using relative attribute to describe the abundance of mixed pixel as relative purity of certain class and learn the abundance with rankSVM. The mixed data sample are used to construct training comparisons set in rankSVM with archetypes generated by the reported Kernel Archetypal Analysis (KAA) unmixing method. In addition, spectral variability is also addressed by constructing comparisons set with synonyms spectrum achieved from KAA. Experiments on both synthetic and real hyperspectral mixed image have demonstrated the potential value of proposed method for mixed pixel analysis.
关键词： relative attribute,spectral unmixing,Hyperspectral image,NMF,spectral variability
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A New Unmixing-Based Approach for Shadow Correction of Hyperspectral Remote Sensing Data
摘要： Hyperspectral remote sensing data are widely used in various applications like classification and target detection. However, recently the influence of shadow has become increasingly greater due to the higher spatial resolution of such data. Shaded areas usually have lower intensity and fuzzy boundary, which make the images hard to interpret automatically. To overcome this issue, shadow correction/compensation of hyperspectral remote sensing data is one of the most used techniques. This process includes in general, the detection and de-shadowing steps. In this work, which focuses only on the de-shadowing step, a new hyperspectral unmixing-based shadow correction/compensation is presented. Experiments are conducted on a real hyperspectral image to evaluate the performance of the proposed approach. Experiments show that the proposed method yields satisfactory de-shadowing results and provides better overall performance compared to another unmixing-based method from the literature.
关键词： linear spectral unmixing,shadow correction/compensation,Hyperspectral imaging
更新于2025-09-10 09:29:36