21 February 2014 Endmember extraction algorithm for hyperspectral image based on PCA-SMACC
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Abstract
Due to the high hyperspectral data volume, high dimensionality and the data itself having great redundancy, the accuracy of Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm is low. In view of this, we proposed an endmember extraction algorithm based on PCA-SMACC. First , it uses principal component analysis(PCA)algorithm to achieve the purpose of hyperspectral data dimensionality reduction. The method removes the data redundancy while maintains the validity of the data. Then it uses SMACC endmember extraction algorithm on the resulting principal component images. The experimental results show that PCA-SMACC algorithm can compensate for the lack of traditional algorithms. Compared with PPI and SMACC algorithms, PCA-SMACC has improved to some extent in the extraction accuracy and speed.
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Chang Liu, Junwei Li, Guangping Wang, "Endmember extraction algorithm for hyperspectral image based on PCA-SMACC", Proc. SPIE 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013, 91421A (21 February 2014); doi: 10.1117/12.2054028; https://doi.org/10.1117/12.2054028
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