Version 1
: Received: 16 January 2024 / Approved: 17 January 2024 / Online: 17 January 2024 (06:28:26 CET)
How to cite:
Song, W.; Zhang, X.; Chen, Y.; Xu, H.; Wang, L.; Wang, Y. Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning. Preprints2024, 2024011274. https://doi.org/10.20944/preprints202401.1274.v1
Song, W.; Zhang, X.; Chen, Y.; Xu, H.; Wang, L.; Wang, Y. Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning. Preprints 2024, 2024011274. https://doi.org/10.20944/preprints202401.1274.v1
Song, W.; Zhang, X.; Chen, Y.; Xu, H.; Wang, L.; Wang, Y. Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning. Preprints2024, 2024011274. https://doi.org/10.20944/preprints202401.1274.v1
APA Style
Song, W., Zhang, X., Chen, Y., Xu, H., Wang, L., & Wang, Y. (2024). Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning. Preprints. https://doi.org/10.20944/preprints202401.1274.v1
Chicago/Turabian Style
Song, W., Lianchao Wang and Yinggang Wang. 2024 "Dimensionality Reduction and Research of Hyperspectral Remote Sensing Images Based on Manifold Learning" Preprints. https://doi.org/10.20944/preprints202401.1274.v1
Abstract
With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.
Keywords
hyperspectral remote sensing; manifold learning; local tangent space alignment; spectral angle mapping; spectral information divergence; dimensionality reduction; feature extraction
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
