Multilevel Reweighted Sparse Hyperspectral Unmixing Using Superpixel Segmentation and Particle Swarm Optimization

Miao, Yapeng; Yang, Bin

doi:10.1109/lgrs.2022.3203990

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The fitting accuracy of the presented study is in the same order of magnitude as the results presented in [46][47][48]. These studies, however, reported a higher number of endmembers (e.g., 4-5).…”

Section: Discussionsupporting

confidence: 80%

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Moncholi-Estornell,

Cendrero-Mateo,

Antala

et al. 2023

Remote Sensing

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Solar-induced chlorophyll fluorescence (SIF) is closely related to plant photosynthetic activity and has been used in different studies as a proxy for vegetation health status. However, in order to use SIF as a relevant indicator of plant physiological stress, it is necessary to accurately quantify the amount of light absorbed by the photosynthetic plant pigments, called the absorbed photosynthetically active radiation (APAR). The ratio between fluorescence emission and light absorption (i.e., SIF and APAR) is known as the fluorescence quantum efficiency (FQE). In this work, simultaneous measurements of SIF and reflected radiance were performed both at the leaf and canopy levels for Salvia farinacea and Datura stramonium plants. With the aim of disentangling the proportion of sunlit and shaded absorbed PAR, an ad hoc experimental setup was designed to provide a wide range of fraction vegetation cover (FVC) canopy settings. A linear spectral unmixing method was proposed to estimate the contribution of soil, sunlit, and shaded vegetation from the total reflectance spectrum measured at the canopy level. Later, the retrieved sunlit FVC (FVCsunlit) was used to estimate the (dominant) green APAR flux, and this was combined with the integral of the spectrally resolved fluorescence to calculate the FQE. The results of this study demonstrated that under no-stress conditions and independently of the FVC, similar FQE values were observed when SIF was properly normalised by the green APAR flux. The results obtained showed that the reflectance spectra retrieved using a linear unmixing method had a maximum RMSE of less than 0.03 along the spectrum. The FVCsunlit evaluation showed an RMSE of 14% with an R2 of 0.84. Moreover, the FQE values obtained at the top of the canopy (TOC) were found statistically comparable to the reference values at the leaf level. These results support further efforts to improve the interpretation of fluorescence based on field spectroscopy and the further upscaling to imaging spectroscopy at airborne and satellite levels.

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Section: Discussionsupporting

confidence: 80%

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Moncholi-Estornell,

Cendrero-Mateo,

Antala

et al. 2023

Remote Sensing

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“…Considering the high-dimensional characteristics of hyperspectral unmixing, our previous work [49] developed a divide-and-conquer-based strategy to efficiently exchange particles' historical and global optimal information in different swarms according to the dimensions divided using the indices of pixels or bands. The dimensional division-based multi-swarm PSO can estimate particles' positions (i.e., the optimal solutions) more accurately in a finer search mode and work as a general framework to generate reliable results for different unmixing tasks [47,57]. Hence, the proposed method adopts this improved PSO framework for optimization.…”

Section: Alternating Update Of Unmixing Variables Via Multi-swarm Psomentioning

confidence: 99%

Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation

Yin,

Yang

2023

Remote Sensing

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In hyperspectral unmixing, dealing with nonlinear mixing effects and spectral variability (SV) is a significant challenge. Traditional linear unmixing can be seriously deteriorated by the coupled residuals of nonlinearity and SV in remote sensing scenarios. For the simplification of calculation, current unmixing studies usually separate the consideration of nonlinearity and SV. As a result, errors individually caused by the nonlinearity or SV still persist, potentially leading to overfitting and the decreased accuracy of estimated endmembers and abundances. In this paper, a novel unsupervised nonlinear unmixing method accounting for SV is proposed. First, an improved Fisher transformation scheme is constructed by combining an abundance-driven dynamic classification strategy with superpixel segmentation. It can enlarge the differences between different types of pixels and reduce the differences between pixels corresponding to the same class, thereby reducing the influence of SV. Besides, spectral similarity can be well maintained in local homogeneous regions. Second, the polynomial postnonlinear model is employed to represent observed pixels and explain nonlinear components. Regularized by a Fisher transformation operator and abundances’ spatial smoothness, data reconstruction errors in the original spectral space and the transformed space are weighed to derive the unmixing problem. Finally, this problem is solved by a dimensional division-based particle swarm optimization algorithm to produce accurate unmixing results. Extensive experiments on synthetic and real hyperspectral remote sensing data demonstrate the superiority of the proposed method in comparison with state-of-the-art approaches.

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“…Among many MHAs, PSO is a formidable nature-inspired meta-heuristic algorithm under the swarm intelligence (SI) umbrella and can be effectively used for solving challenging multi-objective optimization problems [13]. Due to its global optimization ability and calibratable parameter setting, it has been successfully applied in different industrial applications and multidisciplinary scientific and engineering research areas, including feature selection [25], image segmentation [26], static and dynamic clustering [27], deep neural network research [28], multi-objective control [29], and many others. In view of this, high-performance solutions to TSCC problems may be derived very effectively by using PSO [30]- [33].…”

Section: Introductionmentioning

confidence: 99%

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Gad

Houssein

Zhou

et al. 2024

IEEE Internet Things J.

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In recent years, ongoing advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is one of the difficulties of cloud computing, which is typically referred to as a Task-Scheduling-in-Cloud-Computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this paper introduces an Opposition-based Simulated Annealing Particle Swarm Optimizer (OSAPSO) to address PSO's premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, the swarm is formed at random by using OBL to guarantee swarm diversity with a light computational burden. A multi-way tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion-scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good trade-off between solution diversity and convergence speed of the proposed method. Due to the non-convex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multi-scale IIoT heterogeneous task groups (realistic and synthetic). In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO has a winning performance and is statistically more This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment.

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Multilevel Reweighted Sparse Hyperspectral Unmixing Using Superpixel Segmentation and Particle Swarm Optimization

Cited by 6 publications

References 19 publications

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

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