Semi-Supervised Unmixing of Hyperspectral Data via Spectral-Spatial Factorization

Tan, Xintong; Yu, Qi; Wang, Zelong; Zhu, Jun

doi:10.1109/jsen.2021.3118885

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…This strategic fusion optimally harnesses the strengths of both labeled and unlabeled data, resulting in a substantial enhancement in the accuracy of change detection algorithms. In hyperspectral unmixing, both labeled and unlabeled data can be used in a semi-supervised manner to improve the accuracy of decomposing mixed hyperspectral pixels into their constituent spectral signatures or endmembers [39]. Thus, the pioneering achievements of semi-supervised deep learning in computer vision and the initial successes in hyperspectral analysis highlight the imperative need to fully exploit the information linkage between labeled and unlabeled data.…”

Section: A Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

Huang,

Zhang,

et al. 2024

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

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Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current deep learning-based approaches often neglect critical feature interactions across temporal, spatial, and spectral domains. To address these challenges, we introduce a semisupervised model called the HyperMatch-based Cross Temporal and Spatial Interaction Transformer (CTSIT) for hyperspectral change detection. The key contributions of this study are as follows: 1) Introduction of the HyperMatch training schedule, which relies on weak-to-strong consistency, to enhance feature extraction using both labeled and unlabeled data. 2) Contribution of the Cross Temporal Bidirectional Attention (CTBA) module to emphasize bidirectional temporal interactions and resemblances. 3) Introduction of the Spatial and Spectral Attention (SSA) module, which includes the Dense Cross Spatial Attention (DCSA) and Spectral Attention (SA) modules, to capture longrange densely spatial interactions and internal spectral similarities. Extensive comparative experiments conducted on three mainstream hyperspectral change detection datasets confirm the effectiveness and superiority of the proposed HyperMatch-based CTSIT in utilizing both labeled and unlabeled samples. For instance, the training ablation demonstrates that this method significantly outperforms most existing state-of-the-art (SOTA) hyperspectral change detection methods, even with a significantly smaller number of samples.

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Section: A Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

Huang,

Zhang,

et al. 2024

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

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“…Therefore, a great deal of excellent research and exploration on NMF has been carried out in recent years [13]- [16]. However, due to the non-convex nature of the NMF model, the solution space of the objective function is extremely large, so the standard NMF is not suitable for direct application to unmixing.…”

Section: Introductionmentioning

confidence: 99%

A Fast Sparse NMF Optimization Algorithm for Hyperspectral Unmixing

Qu,

2024

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

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Hyperspectral remote sensing images (HSIs) have received extensive attention because of their high spectral resolution. However, the limitation of spatial resolution of imaging spectrometers results in a large number of mixed pixels, which restricts the accuracy of data processing. Consequently, hyperspectral unmixing (HU) becomes an important step and tool in remote sensing image processing. In recent years, many approaches based on sparse nonnegative matrix factorization (NMF) have been widely applied in the field of hyperspectral unmixing due to their explicit statistical properties. In particular, methods based on 1/2 norm regularized NMF and their extended models have attracted considerable attention. However, the 1/2 sparse regularizer leads to a nonconvex and nonsmooth problem, which is a significant challenge to solve quickly and efficiently in practice. In this paper, to solve the above problems, we propose a new fast 1/2 sparse NMF algorithm, called NewSpr-NMF, which combines two famous fast gradient strategies, i.e., Barzilai-Borwein gradient and Nesterov's optimal gradient, to alternately optimize two subproblems in NMF framework under the block coordinate descent scheme. Specifically, for the abundance subproblem of sparse NMF, in order to accelerate the solving process, we use Barzilai-Borwein gradient technology to develop a half-thresholding approximate iterative subalgorithm, while for another endmember subproblem, we adopt the Nestrov's optimal gradient method with second-order convergence to solve. Finally, we integrate the two aforementioned algorithms together to achieve efficient unmixing by alternately optimizing the two subproblems of the sparse NMF. The computational complexity and convergence of the proposed algorithm are carefully analysed and briefly proved theoretically, respectively. A series of experimental results on both synthetic and real datasets, and compared with other advanced approaches, verify the effectiveness and efficiency of the proposed algorithm.

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Spatial-Spectral Nonlinear Hyperspectral Unmixing Under Complex Noise

Jing

Sui

et al. 2022

IEEE Sensors J.

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Semi-Supervised Unmixing of Hyperspectral Data via Spectral-Spatial Factorization

Cited by 5 publications

References 45 publications

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

A Fast Sparse NMF Optimization Algorithm for Hyperspectral Unmixing

Spatial-Spectral Nonlinear Hyperspectral Unmixing Under Complex Noise

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