This paper is published in Volume-4, Issue-3, 2018
Area
Image Processing
Author
Pavithra Sukumar
Co-authors
Sreena V G
Org/Univ
Marian Engineering College, Thiruvananthapuram, Kerala, India
Pub. Date
09 May, 2018
Paper ID
V4I3-1303
Publisher
Keywords
HSI (Hyperspectral Image), Spectral reflectance curve, Pure pixel, Mixed pixel, Endmember, PPI (Pixel Purity Index), LMM (Linear Mixing Model)

Citationsacebook

IEEE
Pavithra Sukumar, Sreena V G. Classification of hyperspectral images using PPI and LMM, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Pavithra Sukumar, Sreena V G (2018). Classification of hyperspectral images using PPI and LMM. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Pavithra Sukumar, Sreena V G. "Classification of hyperspectral images using PPI and LMM." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Hyperspectral images are the treasure of information since it contains hundreds of spectral bands. Classification of Hyperspectral images is the process of identifying the components in each pixel. For this purpose, the pure and mixed pixels of the image should be identified and the endmember signatures and components are identified with the help of spectral libraries. In this paper, it is attempted to identify the minerals in the hyperspectral data ‘Cuprite’, that covers the Cuprite mines in Las Vegas, Nevada, the U.S. The pure pixels are identified by using PPI algorithm and their endmember signatures are obtained. The abundance maps of mixed pixels are obtained by using LMM. The total variation based regularization and joint sparsity of abundance maps are exploited in this paper.
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