Dimensionality Reduction of Hyperspectral Image Using Different Methods
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Vidyasagar Talwar, Prof. B.S.Sohi
|DOI : 10.14445/22315381/IJETT-V69I2P220|
MLA Style: Vidyasagar Talwar, Prof. B.S.Sohi "Dimensionality Reduction of Hyperspectral Image Using Different Methods" International Journal of Engineering Trends and Technology 69.2(2021):139-143.
APA Style:Vidyasagar Talwar, Prof. B.S.Sohi. Dimensionality Reduction of Hyperspectral Image Using Different Methods. International Journal of Engineering Trends and Technology, 69(2), 139-143.
The Hyperspectral images (HSI) are images obtained across the electromagnetic spectrum. Basically, images having a greater number of dimensions and complexity in processing and analyzing the data. As the number of dimensionalities increases, its accuracy gets decreases. Hence it is necessary to reduce the dimensionality by applying a pre-processing step. This HSI is widely used in industries and technology like remote sensing, seed viability study, biotechnology, environment monitoring, food, pharmaceuticals, medical diagnose, forensic, thin films, oil, and gas. There are different methods to reduce the dimensionality of these images like Principal component analysis (PCA), Weighted sparse graph-based (WSG), Curvilinear component analysis (CCA), Fractal based, Independent component analysis, Empirical mode and wavelets, Embedding, Band selection, Component analysis, Neighbourhood
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Hyperspectral image; Dimensionality reduction; different methods to reduce the dimensionality; principal component analysis.