Analysis of Dimensionality Reduction Techniques for Hyperspectral Image Classification
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Sinduja.R, Prof.S.Chidambaram, Dr.A.Sumathi
|DOI : 10.14445/22315381/IJETT-V21P219|
Sinduja.R, Prof.S.Chidambaram, Dr.A.Sumathi"Analysis of Dimensionality Reduction Techniques for Hyperspectral Image Classification", International Journal of Engineering Trends and Technology (IJETT), V21(2),111-115 March 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Hyperspectral imagery utilized in remote sensing applications provides richer data concerning materials than multispectral imagery. The new larger information volumes from hyperspectral sensors gift a challenge for ancient process techniques. as an example, the identification of every ground surface picture element by its corresponding spectral signature remains tough as a result of the large volume of knowledge. standard classification strategies might not be used while not dimension reduction pre-processing. this is often as a result of the curse of spatiality, that refers to the very fact that the sample size required to estimate a perform of many variables to a given degree of accuracy grows exponentially with the quantity of variables. Principal part analysis (PCA) has been the technique of alternative for dimension reduction. However, PCA is computationally high-ticket and doesn't eliminate anomalies which will be seen at one arbitrary band. The high-dimensional nature of the information collected by such sensors not solely will increase procedure complexness however can also degrade classification accuracy. to deal with this issue, spatiality reduction (DR) has become a crucial aid to rising classifier potency on these pictures. Dimension reduction algorithms don't scale back the dimension of knowledge with the goal of reconstructing AN approximation to the initial signal. Instead, they ask for a minimal illustration of the signal that sufficiently retains the requisite data for prosperous unmixing within the lower dimension. Dimension reduction algorithms are designed to attenuate errors within the procedures performed within the lower dimension.
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PCA is computationally high-ticket and doesn't eliminate anomalies which will be seen at one arbitrary band.