Dimensionality reduction using superpixel segmentation for hyperspectral unmixing using the cNMF

Jiarui Yi; Miguel Velez-Reyes

doi:10.1117/12.2264345

5 May 2017 Dimensionality reduction using superpixel segmentation for hyperspectral unmixing using the cNMF

Jiarui Yi, Miguel Velez-Reyes

Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101981H (2017) https://doi.org/10.1117/12.2264345
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

This paper presents an approach to reduce dimensionality for hyperspectral unmixing using superpixel segmentation. The dimensionality reduction is achieved by over-segmenting the hyperspectral image using superpixels that are used as a reduced subset of representative pixels for the full hyperspectral image. Once superpixel are extracted, endmember extraction methods are applied to the reduced spectral data set with clear computational advantages. The proposed method is illustrated on the AVIRIS image captured over Fort AP Hill, Virginia. A comparison of the method with standard unmixing techniques is also included.

Citation Download Citation

Jiarui Yi and Miguel Velez-Reyes "Dimensionality reduction using superpixel segmentation for hyperspectral unmixing using the cNMF", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101981H (5 May 2017); https://doi.org/10.1117/12.2264345

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