- 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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Estimation of the number of endmembers in hyperspectral data using a weight-sequence geometry method
摘要： The terrestrial reflection or emission spectrum obtained by the remote sensor is recorded in units of pixels. In most cases, a pixel usually contains many types of terrains. This pixel is a mixed pixel, and each of the terrains in the mixed pixels is called 'endmember'. Estimating the number of endmembers is a significant step in many hyperspectral data mining techniques, such as target classification and endmember extraction. The paper proposes a separative detection method by the use of a weight-sequence geometry to estimate the number of endmembers. This method projects the spectral matrix into the orthogonal subspace by eigenvalue decomposition at first. Then, on the basis of the normalized eigenvalue sequence, the separative detection method innovatively uses a geometric criterion to find the separation point between the main factors and minor factors. Finally, the number of endmembers is determined by the sequence of the 'separation point'. Validation through a series of simulated and real hyperspectral data, it indicates that the proposed method can accurately and rapidly detect the number of endmembers in the hyperspectral data without any prior information. In addition, the new method is also applicable to the ultra-high resolution remote spectral data in the future.
关键词： number of endmembers,Hyperspectral data,separative detection method,hyperspectral unmixing
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Robust Nonnegative Local Coordinate Factorization for Hyperspectral Unmixing
摘要： Recently, nonnegative matrix factorization (NMF) has become increasingly popular for hyperspectral unmixing (HU). Due to the non-convex nature of the NMF theory, which is sensitive to the initial value and various noise. To obtain more accurate and robust unmixing model, in this paper, we propose a novel method called robust nonnegative local coordinate factorization (RNLCF). RNLCF adds a local coordinate constraint into the composite loss function which combing classic and Correntropy Induced Metric NMF function. To solve RNLCF, we developed a multiplicative update rules. Experimental results on synthetic and real-world data verify the effectiveness of RNLCF comparing with the representative methods.
关键词： local coordinate,Correntropy Induced Metric,hyperspectral unmixing (HU),nonnegative matrix factorization (NMF)
更新于2025-09-23 15:22:29
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Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data
摘要： High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature.
关键词： photovoltaic panels,detection and area estimation,urban areas,hyperspectral unmixing,hyperspectral imaging,partial nonnegative matrix factorization
更新于2025-09-16 10:30:52
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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 - Simultaneous Dictionary Sparse Pruning and Collaborative Sparse Regression for Hyperspectral Image Unmixing
摘要： Recently, the dictionary-aided sparse regression (SR) method for hyperspectral unmixing has received much attention in the field of remote sensing. However, under the assumption that each pixel in the hyperspectral scene can be viewed as a combination of endmembers in the spectral library, most of SR methods ignore the spectral signature mismatches between an actual spectral signature and its corresponding endmember in spectral library. To overcome this problem, we proposed a joint optimizing unmixing model called DSPCSR which includes dictionary sparse pruning and collaborative sparse regression. By exploiting the sparse property of spectral mismatch error and the collaborative sparse property of the abundance matrix, the DSPCSR can provide good robustness and performance. Experiments on the synthetic and real datasets show that the proposed DSPCSR can achieve better performance compared with several state-of-art algorithms.
关键词： semiblind hyperspectral unmixing,dictionary sparse pruning,collaborative sparse regression,compressive sensing,Spectral mismatch
更新于2025-09-10 09:29:36
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Hyperspectral unmixing using double-constrained multilayer NMF
摘要： Hyperspectral unmixing (HU) refers to the process decomposing the entire hyperspectral image into a set of endmembers and the corresponding abundance fractions. Nonnegative matrix factorization (NMF) has been widely used in HU due to its simplicity and effectiveness. Many extensions of NMF have been also developed since traditional NMF has a large solution space. On the other hand, the multilayer structure has shown great advantages in learning data representation. Inspired by these considerations, we added sparsity and geometric structure constraints to the multilayer NMF structure and proposed a double-constrained multilayer NMF (DCMLNMF) method for HU in this paper. The multilayer NMF structure was obtained by iteratively decomposing the target matrix into a number of layers. To improve the unmixing performance, a sparsity constraint term on the abundance matrix and a graph regularization term were both incorporated to each layer. Besides, a layer-wise optimization method based on Nesterov’s optimal gradient method was further proposed to solve the multi-factor NMF problem. Experimental results based on both synthetic data and real data demonstrate that the proposed method outperforms several other state-of-art approaches.
关键词： sparsity constraint,Hyperspectral unmixing,graph regularization,multilayer structure,nonnegative matrix factorization
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 - Constrained Nonnegative Matrix Factorization for Robust Hyperspectral Unmixing
摘要： Hyperspectral unmixng (HU) is an essential step for hyper-spectral image (HSI) analysis. In real HSI, there often are abnormal ?uctuations existing in speci?c bands, which can be described as sparse noise. This type of corruption will se-riously disrupt the hyperspectral image quality, causing extra dif?culties during unmixing process. However, the in?uence of sparse noise is often ignored by existing unmixing meth-ods, which leads to the reduction of robustness and accuracy for HU tasks. Therefore, we propose a new unmixing model which takes noise corruption into consideration. By designing and imposing constraints considering the sparsity of noise, properties of endmember and abundance on nonnegative ma-trix factorization (NMF), the proposed method can resist the sparse noise and achieve more robust and accurate unmixing results. Adequate experiments have been conducted on both synthetic and real hyperspectral data. And the results con?rm the superiority of proposed method compared to state-of-the-art methods.
关键词： sparse noise,Hyperspectral unmixing,nonnegative matrix factorization,robust,constraint
更新于2025-09-10 09:29:36
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Sparse Dictionary Learning for Blind Hyperspectral Unmixing
摘要： Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity. To solve this problem, this paper proposes an efficient algorithm to find all paths of the 1-regularization problem and select the best set of variables for the final abundances estimation. Based on the proposed algorithm, a DL framework is designed for hyperspectral unmixing. Our experimental results indicate that our method performs much better than conventional methods in terms of DL and hyperspectral data reconstruction. More importantly, it alleviates the difficulty of prescribing the sparsity.
关键词： sparse coding,Dictionary learning (DL),hyperspectral unmixing,1-regularization,path algorithm
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Sheffield (2018.7.8-2018.7.11)] 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - New Theory for Unmixing ILL-Conditioned Hyperspectral Mixtures
摘要： Hyperspectral unmixing (HU), a blind source separation problem, aims at unambiguiously identifying the spectral signatures of the materials, as well as their abundances, from the measured hyperspectral mixtures. In real hyperspectral scenes, high correlation between the spectral signatures is commonly observed, making HU quite challenging. Although such ill-conditioning is critical for effective HU, it is often ignored in existing HU literature. To the best of our knowledge, existing preconditioning techniques, for reducing the condition number of the signature matrix, were developed based on the pure-pixel assumption, which can, however, be seriously violated in remote sensing. Under a relaxed purity assumption, with respect to the pure-pixel one, this paper proposes novel theory for unmixing ill-conditioned hyperspectral mixtures. Specifically, we exactly identify the John’s ellipsoid (i.e., the maximum ellipsoid inscribed in the convex hull of the hyperspectral data vectors) via split augmented Lagrangian shrinkage algorithm (SALSA), and transform this ellipsoid into an Euclidean ball. This transformation brings the data vectors into a new space wherein the corresponding material signature vectors form a regular simplex, which is a very strong prior information. Based on this prior, we design an HU criterion, and prove its perfect identifiability under a very mild sufficient condition. Then, we demonstrate the feasibility of realizing our criterion via non-convex optimization and guarantee a stationary point solution.
关键词： split augmented Lagrangian shrinkage algorithm,Hyperspectral unmixing,pure-pixel assumption,non-convex optimization,John’s ellipsoid
更新于2025-09-09 09:28:46
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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 - Deep Auto-Encoder Network for Hyperspectral Image Unmixing
摘要： In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder network composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a variational auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic dataset. In our comparison with other state-of-the-art unmixing methods, the proposed approach demonstrates highly competitive performance.
关键词： Variational auto-encoder,Hyperspectral unmixing,Non-negative sparse auto-encoder,Deep learning
更新于2025-09-04 15:30:14