Dimensionality Reduction for the Feature System in Classification of Hyperspectral Earth Remote Sensing Data by Use of Neural Networks-Reference-Cited by-同舟云学术

Dimensionality Reduction for the Feature System in Classification of Hyperspectral Earth Remote Sensing Data by Use of Neural Networks

Published:2022-02 Issue:1 Volume:58 Page:1-7
ISSN:8756-6990
Container-title:Optoelectronics, Instrumentation and Data Processing
language:en
Short-container-title:Optoelectron.Instrument.Proc.

Author:

Kozik V. I.,Nezhevenko E. S.

Publisher

Allerton Press

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Link

https://link.springer.com/content/pdf/10.3103/S8756699022010046.pdf

Reference9 articles.

1. R. Craig and S. Jie, ‘‘Principal component analysis for hyperspectral image classification,’’ Surv. Land Inf. Syst. 62 (2), 115–122 (2002).

2. E. S. Nezhevenko, ‘‘Neural network classification of difficult-to-distinguish types of vegetation on the basis of hypespectral features,’’ Optoelectron., Instrum. Data Process. 55, 263–270 (2019). https://doi.org/10.3103/S8756699019030087

3. V. I. Kozik and E. S. Nezhevenko, ‘‘Classification of hypespectral images using conventional neural networks,’’ Optoelectron., Instrum. Data Process. 57, 123–131 (2021). https://doi.org/10.3103/S8756699021020102

4. S. M. Borzov and O. I. Potaturkin, ‘‘Selection of the informative feature system for crops classification using hyperspectral data,’’ Optoelectron., Instrum. Data Process. 56, 431–439 (2020). https://doi.org/10.3103/S8756699020040032

5. M. F. Baumgardner, L. L. Biehl, and D. A. Landgrebe, ‘‘220 band AVIRIS hyperspectral image data set: June 12, 1992 Indian Pine Test Site 3,’’ Purdue Univ. Research Repository (2015). https://doi.org/10.4231/R7RX991C