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

Ahmed, Asmau M.; Durán, Olga; Zweiri, Yahya; Smith, Mike

doi:10.3390/rs9080775

Cited by 27 publications

(22 citation statements)

References 74 publications

(111 reference statements)

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“…Developing a cost-effective spectral unmixing method is critical for increasing the estimation accuracy of PVC using remotely-sensed images [20][21][22]. Most spectral unmixing methods have two steps: extraction of endmembers-that is, pure training samples-and estimation of PVC or fraction of vegetation cover.…”

Section: Introductionmentioning

confidence: 99%

“…With simple models and the ability to directly interpret the results, LSU predominates in the area of spectral unmixing. However, the assumption of LSU methods for decomposition of endmembers in mixed pixels is often not true because of multiple scattering from neighboring objects and interactions among the endmembers [19,20]. Moreover, decomposition of endmembers in mixed pixels is complex and depends on many factors, including landscape complexity, spatial resolution of images, purity of endmembers, or training samples selected and relationship of PVC with spectral variables derived from images [5,6,9,11,17,19,20].…”

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confidence: 99%

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A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

et al. 2019

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China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

et al. 2019

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“…With the LMM, it is assumed that the spectra of each mixed pixel are linear combinations of the endmembers contained in the pixel. Despite the fact that it holds only for macroscopic mixture conditions [8,11], it is widely used due to its computational tractability and flexibility in various applications.…”

Section: Introductionmentioning

confidence: 99%

“…In general, endmembers correspond to familiar macroscopic objects in a scene, such as water, metal, and vegetation, as well as constituents of intimate mixtures in microscopic scale. Hyperspectral unmixing can be reconstructed from the linear mixture model (LMM) and nonlinear mixture model [2,[8][9][10]. With the LMM, it is assumed that the spectra of each mixed pixel are linear combinations of the endmembers contained in the pixel.…”

Section: Introductionmentioning

confidence: 99%

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Rizkinia

Okuda

2017

Remote Sensing

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Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2,1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.

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“…The wealth of spectral information in HSIs has opened new perspectives in different applications, such as target detection, spectral unmixing, object classification, and matching [1][2][3][4][5][6][7][8][9][10][11][12][13]. The underlying assumption in object classification techniques is that each pixel comprises the response of only one material.…”

Section: Introductionmentioning

confidence: 99%

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

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Abstract:Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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

Cited by 27 publications

References 74 publications

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

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