TY - GEN
T1 - Dimension Reduction in Hyperspectral Image Using Single Layer Perceptron Neural Network
AU - Bar, Radha Krishna
AU - Mukhopadhyay, Somnath
AU - Chakraborty, Debasish
AU - Hinchey, Mike
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - Hundreds of continuous bands make up a hyperspectral image. All the bands are not equal important. Some of the bands are significant and others are redundant. Band reduction is a typical step before further processing. Instead of attempting to handle the complete information set without losing crucial data, it is essential to select the most valuable bands. Using traditional band selection techniques, the predetermined number of dimensions are selected from the hyperspectral image. In this article, we propose a novel single-layer neural network and a genetic evolutionary approach to reduce a hyperspectral image’s high dimension. The process involves selecting the two bands with the lowest correlation in each iteration and eliminating two redundant bands. The suggested framework eliminates the unnecessary bands from a hyperspectral image and then chooses the ideal number of the most crucial bands.
AB - Hundreds of continuous bands make up a hyperspectral image. All the bands are not equal important. Some of the bands are significant and others are redundant. Band reduction is a typical step before further processing. Instead of attempting to handle the complete information set without losing crucial data, it is essential to select the most valuable bands. Using traditional band selection techniques, the predetermined number of dimensions are selected from the hyperspectral image. In this article, we propose a novel single-layer neural network and a genetic evolutionary approach to reduce a hyperspectral image’s high dimension. The process involves selecting the two bands with the lowest correlation in each iteration and eliminating two redundant bands. The suggested framework eliminates the unnecessary bands from a hyperspectral image and then chooses the ideal number of the most crucial bands.
KW - Band selection
KW - Dimension Reduction
KW - Single Layer Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85180158725&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-48876-4_8
DO - 10.1007/978-3-031-48876-4_8
M3 - Conference contribution
AN - SCOPUS:85180158725
SN - 9783031488757
T3 - Communications in Computer and Information Science
SP - 93
EP - 106
BT - Computational Intelligence in Communications and Business Analytics - 5th International Conference, CICBA 2023, Revised Selected Papers
A2 - Dasgupta, Kousik
A2 - Mukhopadhyay, Somnath
A2 - Mandal, Jyotsna K.
A2 - Dutta, Paramartha
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023
Y2 - 27 January 2023 through 28 January 2023
ER -