Denoising and Dimensionality Reduction for Improving Constrained Energy Minimization in Hyperspectral Imagery Analysis

Shen-En Qian; Guang Yi Chen

doi:10.1364/HMISE.2023.HW5C.2

Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES)
Technical Digest Series (Optica Publishing Group, 2023),
paper HW5C.2
•https://doi.org/10.1364/HMISE.2023.HW5C.2

Denoising and Dimensionality Reduction for Improving Constrained Energy Minimization in Hyperspectral Imagery Analysis

Shen-En Qian and Guang Yi Chen

Hyperspectral/Multispectral Imaging and Sounding of the Environment 2023

Munich Germany
30 July–3 August 2023
ISBN: 978-1-957171-24-1

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Image Processing, Algorithm Development, and Machine Learning (HW5C)

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S. Qian and G. Y. Chen, "Denoising and Dimensionality Reduction for Improving Constrained Energy Minimization in Hyperspectral Imagery Analysis," in Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), Technical Digest Series (Optica Publishing Group, 2023), paper HW5C.2.

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Abstract

A new hyperspectral objects detection method is presented. It adaptively reduces noises of a hyperspectral image followed by dimensionality reduction before generating a Constrained Energy Minimization (CEM) map for detection. Experimental results show that the method improves CEM detection accuracy significantly for tested hyperspectral images.

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