Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

Zhang, Ge; Mei, Shaohui; Ma, Mingyang; Feng, Yan

doi:10.1109/tgrs.2022.3169228

Cited by 20 publications

(4 citation statements)

References 66 publications

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“…When DWT is combined with LSTM and DRNN, problems like gradient vanishing or exploding are successfully resolved, maintaining information fidelity and keeping some historical data. PSNR and MSE, are three performance evaluation metrics that show how much better the suggested DWT-RNN approach is than the state-ofthe-art techniques like LSMA-based compression [23], STW-WDR [24], and DPCM [25]. In comparison to other methods, the higher PSNR value of 45 dB and lower MSE of 7.50% show better compression efficiency and superior image quality.…”

Section: B Discussionmentioning

confidence: 99%

Efficient Compression for Remote Sensing: Multispectral Transform and Deep Recurrent Neural Networks for Lossless Hyper-Spectral Imagine

Anuradha,

Sekhar,

Mishra

et al. 2024

IJACSA

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Remote sensing technologies, which are essential for everything from environmental monitoring to disaster relief, enable large-scale multispectral data collection. In the field of hyper-spectral imaging, where high-dimensional data is required for precise analysis, effective compression techniques are critical for transmission and storage. In the field of hyper-spectral imaging, the development of efficient compression techniques is critical because datasets containing high-dimensional information must be transmitted and stored efficiently without sacrificing analytical precision. The paper presents advanced compression techniques that combine deep Recurrent Neural Networks (RNNs) with multispectral transforms to achieve lossless compression in hyper-spectral imaging. The Discrete Wavelet Transform (DWT) is used to efficiently capture spectral and spatial information by utilizing the properties of multispectral transforms. Simultaneously, deep RNNs are used to model the hyper-spectral data with complex dependencies, allowing for sequential compression. The overall compression efficiency that is increased by the integration of spatial and spectral information allows for reduced storage requirements and improved transmission efficiency. Python software is used to implement the proposed model. When compared to Liner Spectral Mixture Analysis (LSMA) based compression, Spatial Orientation Tree Wavelet (STW)-Wavelet Difference Reduction (WDR), and DPCM, the proposed DWT-RNN-LSTM method has a better PSNR value of 45 dB and a lower MSE of 7.50%. Adaptive compression methods are presented in order to dynamically adapt to various data properties and ensure application in various hyperspectral scenes. Studies on hyperspectral images of various sizes and resolutions demonstrate the approach's scalability and generalization, as well as the utility and adaptability of the proposed compression framework in a variety of remote sensing scenarios.

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Section: B Discussionmentioning

confidence: 99%

Efficient Compression for Remote Sensing: Multispectral Transform and Deep Recurrent Neural Networks for Lossless Hyper-Spectral Imagine

Anuradha,

Sekhar,

Mishra

et al. 2024

IJACSA

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“…Representative approaches with the first manner involve the nonnegative matrix factorization-based algorithms [8][9], deep learning-based algorithms [10][11][12], and unmixing algorithms considering spectral variability [13][14][15]. The second way estimates the abundance based on existent endmembers, frequently used are the sparse regression-based algorithms [16][17][18], and the multiple endmember spectral mixture analysis-based algorithms [19][20]; endmembers can be either acquired from an existing library or automatically collected from the image [21][22]. Image-based endmembers are more accessible and affordable than library-endmembers.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Evolutionary Algorithm Based on Subspace Specified Searching for Hyperspectral Endmember Extraction

Lei,

Liu,

Tian

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, the introduction of multi-objective evolutionary algorithms (MOEAs) into the field of endmember extraction in hyperspectral unmixing has demonstrated a breadth of results that surpass those derived from single objective-based methodologies. Despite these advancements, the adaptation of MOEAs to endmember extraction, and the attainment of globally optimal solutions, represent unresolved challenges meriting continued exploration. This study addresses two principal obstacles in MOEA-based endmember extraction: the notorious "curse of dimensionality" in high-dimensional optimization, and the difficulty in striking a balance between convergence and population diversity. We propose a two-stage, evolutionary-based endmember extraction algorithm, referred to as TSEA, designed to confront these issues. A novel solution space splitting (3S) strategy is incorporated into TSEA that efficiently mitigates the curse of dimensionality by strategically contracting the search space. This advantage is largely attributed to the significant reduction of invalid solutions achieved through the simple application of a clustering procedure. Furthermore, a two-stage optimization approach is employed to meticulously uphold the convergence and diversity of the population, aiming to attain the optimal solution within the realm of highdimensional optimization. Empirical evidence from four real hyperspectral images demonstrates that the proposed TSEA outperforms other comparison multi-objective optimization algorithms. Thus, this study contributes to the ongoing discourse on the optimization and applicability of MOEAs in the context of endmember extraction.

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“…For example, in vegetation-covered areas and complex urban scenes, there are nonlinear effects caused by multiple reflections between endmembers, i.e., materials that are spectrally unique in the wavelength bands used to collect the image [7,8]. There is also spectral variability due to illumination conditions, atmospheric effects and intrinsic variability in the properties of the pure material [9][10][11][12]. This indicates that it is not sufficient to simply generalize the use of linear models and that spectral variability must also be taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Augmented GBM Nonlinear Model to Address Spectral Variability for Hyperspectral Unmixing

et al. 2023

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Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the scene. To address that issue, we consider the effects of SV on SU while investigating the nonlinear effects of hyperspectral images. Furthermore, an augmented generalized bilinear model is proposed to address spectral variability (abbreviated AGBM-SV). First, AGBM-SV adopts a generalized bilinear model (GBM) as the basic framework to address the nonlinear effects caused by second-order scattering. Secondly, scaling factors and spectral variability dictionaries are introduced to model the variability issues caused by the illumination conditions, material intrinsic variability, and other environmental factors. Then, a data-driven learning strategy is employed to set sparse and orthogonal bases for the abundance and spectral variability dictionaries according to the distribution characteristics of real materials. Finally, the alternating direction method of multipliers (ADMM) optimization method is used to split and solve the objective function, enabling the AGBM-SV algorithm to estimate the abundance and learn the spectral variability dictionary more effectively. The experimental results demonstrate the comparative superiority of the AGBM-SV method in both qualitative and quantitative perspectives, which can effectively solve the problem of spectral variability in nonlinear mixing scenes and to improve unmixing accuracy.

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Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

Cited by 20 publications

References 66 publications

Efficient Compression for Remote Sensing: Multispectral Transform and Deep Recurrent Neural Networks for Lossless Hyper-Spectral Imagine

Efficient Compression for Remote Sensing: Multispectral Transform and Deep Recurrent Neural Networks for Lossless Hyper-Spectral Imagine

Two-Stage Evolutionary Algorithm Based on Subspace Specified Searching for Hyperspectral Endmember Extraction

Augmented GBM Nonlinear Model to Address Spectral Variability for Hyperspectral Unmixing

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