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Effect of Denoising on Dimensionally Reduced Sparse Hyperspectral Unmixing
- Source :
- Procedia Computer Science. 115:391-398
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 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.
- Subjects :
- business.industry
Computer science
Noise reduction
Dimensionality reduction
0211 other engineering and technologies
Hyperspectral imaging
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Reduction (complexity)
Dimension (vector space)
Computer Science::Computer Vision and Pattern Recognition
Full spectral imaging
0202 electrical engineering, electronic engineering, information engineering
General Earth and Planetary Sciences
Computer vision
Artificial intelligence
business
Image resolution
021101 geological & geomatics engineering
General Environmental Science
Parametric statistics
Subjects
Details
- ISSN :
- 18770509
- Volume :
- 115
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi...........7b689dc40f4c1fc2e04c0bd7eac79061