ON DIMENSION REDUCTION OF HYPERSPECTRAL IMAGES

被引：0

作者：

Race, Benjamin ^{[1
]}

Wittman, Todd ^{[1
]}

机构：

[1] Citadel, Dept Math Sci, Charleston, SC 29409 USA

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Hyperspectral images; dimensionality reduction; anomaly detection; spectral unmixing; classification;

D O I：

10.1109/IGARSS52108.2023.10281535

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Many dimension reduction (DR) methods have been proposed to deal with the large dimensions of hyperspectral images, but it is unclear which algorithm works best in a given situation. We compare the performance of several DR methods for remote sensing applications. The methods are evaluated by applying them to common datasets and then performing the common image processing tasks of classification, anomaly detection, and spectral unmixing. We evaluate the results against ground truth data and make recommendations for choosing a DR method for certain situations.

引用

页码：7396 / 7399

页数：4

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