A comparison of dimensionality reduction techniques for hyperspectral imagery

Benjamin Race; Todd Wittman

doi:10.1117/12.2632014

31 May 2022 A comparison of dimensionality reduction techniques for hyperspectral imagery

Benjamin Race, Todd Wittman

Proceedings Volume 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII; 1209412 (2022) https://doi.org/10.1117/12.2632014
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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Abstract

Due to the high-dimensional nature of hyperspectral images, dimensionality reduction techniques can improve image analysis by reducing memory requirements, making image processing techniques more efficient, and sometimes increasing the accuracy of the results. A wide variety of dimensionality reduction (DR) algorithms have been proposed, ranging from linear transformations like Principal Component Analysis (PCA) to more computationally intensive manifold learning algorithms such as Locally Linear Embedding (LLE). The best dimensionality reduction technique and the ideal dimensionality depend on the dataset and the image processing task under consideration. This paper compares various dimensionality reduction methods by evaluating the performance of image analysis tasks on available hyperspectral datasets with ground truth reference.

Citation Download Citation

Benjamin Race and Todd Wittman "A comparison of dimensionality reduction techniques for hyperspectral imagery", Proc. SPIE 12094, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII, 1209412 (31 May 2022); https://doi.org/10.1117/12.2632014

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