Publication Type : Conference Proceedings
Publisher : 7th International Conference on Advances in Computing and Communications , ICACC-2017, Elsevier,
Source : 7th International Conference on Advances in Computing and Communications , ICACC-2017, Elsevier, Volume 115, Rajagiri School of Engineering & Technology, p.391-398 (2017)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85032452805&origin=resultslist&sort=plf-f&src=s&sid=18140dd158b59fc89f538e9f430579a7&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2836096164300%29&relpos=38&citeCnt=1&searchTerm=
Keywords : Denoising, Dimensionality reduction, Hyperspectral images, Least Square, separated by semicolons, Spectral unmixing, Type your keywords here
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2017
Abstract : In hyperspectral images, spectral mixing occurs when objects lying beside each cannot be distinguished as different entities due to its low spatial resolution. Other hurdles in hyperspectral imaging are its huge dimension and noisy bands. In this paper, a new approach for spectral unmixing is presented where, the data is reduced dimensionally and, the bands eliminated during this are denoised using the existing denoising methods. Then, dataset with these bands is dimensionally reduced and their presence after reduction is validated using spectral unmixing methods. The effectiveness of this method is evaluated using parametric measures such as RMSE and classification accuracy. © 2017 The Author(s).
Cite this Research Publication : Swarna .M, Sowmya, and Dr. Soman K. P., “Effect of Denoising on Dimensionally Reduced Sparse Hyperspectral Unmixing”, 7th International Conference on Advances in Computing and Communications , ICACC-2017, vol. 115. Elsevier, Rajagiri School of Engineering & Technology, pp. 391-398, 2017.