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In this paper (within space constraint), we review the learning-based approaches and provide deep learning perspective to the spectral unmixing.
Jun 9, 2021 · In remote sensing, hyperspectral unmixing is very challenging inverse ill-posed problem which does not have closedform solution.
We discuss spec- tral unmixing using three deep learning architectures, viz., autoencoders, convolutional neural network, and generative model. Further, we ...
This paper discusses spectral unmixing using three deep learning architectures, viz., autoencoders, convolutional neural network, and generative model, ...
The DL-based unmixing methods' main advantage is that they perform better in learning valuable hidden features of abundances from hyperspectral data directly ...
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Oct 28, 2024 · This paper has introduced two deep learning architectures that outperform prior methods for attenuation correction and unmixing of hyperspectral ...
Deep Learning (DL) techniques have demonstrated remarkable efficacy in various HSI analysis tasks, including those within agriculture.
Dec 21, 2024 · ... Analysis (PCA) or Autoencoders are often employed to reduce the dimensionality of hyperspectral data, simplifying it for deep learning models.
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This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most ...
Oct 12, 2023 · In this study, the performance of Deep Learning methods using AE for SU is evaluated, and their results are compared with traditional methods.