Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model

Chen, Fen; Wang, Ke; Tang, Ting

doi:10.1109/tgrs.2016.2574331

Cited by 21 publications

(16 citation statements)

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“…SMA is also called a pixel unmixing model, and it overcomes the limitations of the empirical method for estimating FVC over a large area [13]. SMA was developed based on early vegetation micrometeorological modeling [12,14,15], and extended some similar algorithms, e.g., the pixel dichotomy model [1,16].…”

Section: Introductionmentioning

confidence: 99%

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

Ding

et al. 2021

Remote Sensing

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With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

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Section: Introductionmentioning

confidence: 99%

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

Ding

et al. 2021

Remote Sensing

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“…Fan and Deng [27] proposed SASD-MESMA, which first selects the best candidate endmembers for each individual pixel based on both spectral angle and spectral distance measures before entering the unmixing stage. Chen et al [28] apply a similar concept but use sparse unmixing rather than spectral similarity measures during the pruning step. Given the size of a generic spectral library, in combination with the high number of pixels within (especially high resolution) imagery, adopting a pixel-wise library pruning strategy may prove to be computationally inefficient.…”

Section: Introductionmentioning

confidence: 99%

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

et al. 2017

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Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the "Automated MUsic and spectral Separability based Endmember Selection technique" (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows.

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“…where a is vectorized true abundances of all pixels,â is vectorized estimated abundances of all pixels. To evaluate the sparsity level (SL) induced by the methods, one monitors [19,20] SL ≡ 1 P…”

Section: Validation Of Methodsmentioning

confidence: 99%

“…whereâ i is the estimated abundances of the ith pixel, P is the number of pixels. Finally, one considers the distance between the two actual and estimated supports [20,21])…”

Section: Validation Of Methodsmentioning

confidence: 99%

A multiple endmember mixing model to handle spectral variability in hyperspectral unmixing

Uezato

Fauvel

Dobigeon

2018

2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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This paper proposes a novel mixing model that incorporates spectral variability. The proposed approach relies on the following two ingredients: i) a mixed spectrum is modeled as a combination of a few endmember signatures which belong to some endmember bundles (referred to as classes), ii) sparsity is promoted for the selection of both endmember classes and endmember spectra within a given class. This leads to an adaptive and hierarchical description of the endmember spectra. A proximal alternating linearized minimization algorithm is derived to minimize the objective function associated with this model, providing estimates of the bundling coefficients and abundances. Results showed that the proposed method outperformed the existing methods in terms of promoting sparsity and selecting endmember classes within each pixel.

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Spectral Unmixing Using a Sparse Multiple-Endmember Spectral Mixture Model

Cited by 21 publications

References 50 publications

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

A multiple endmember mixing model to handle spectral variability in hyperspectral unmixing

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